Credit and business cycles: Causal effects in the frequency domain

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Notes: The figure depicts time series of the spot exchange rate Colombian Peso/USD (continuous line) and the level consistent with the PPP fundamental (dashed line) in the sample period June 25, 2008 to December 30, 2013." ] ] ] "autores" => array:1 [ 0 => array:2 [ "autoresLista" => "Juan David Durán-Vanegas" "autores" => array:1 [ 0 => array:2 [ "nombre" => "Juan David" "apellidos" => "Durán-Vanegas" ] ] ] ] ] "idiomaDefecto" => "en" "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S012044831500041X?idApp=UINPBA00004N" "url" => "/01204483/0000003300000078/v2_201703210056/S012044831500041X/v2_201703210056/en/main.assets" ] "en" => array:19 [ "idiomaDefecto" => true "titulo" => "Credit and business cycles: Causal effects in the frequency domain" "tieneTextoCompleto" => true "paginas" => array:1 [ 0 => array:2 [ "paginaInicial" => "176" "paginaFinal" => "189" ] ] "autores" => array:1 [ 0 => array:4 [ "autoresLista" => "Jose Eduardo Gomez-Gonzalez, Mauricio Villamizar-Villegas, Hector Manuel Zarate, Juan Sebastian Amador, Celina Gaitan-Maldonado" "autores" => array:5 [ 0 => array:3 [ "nombre" => "Jose Eduardo" "apellidos" => "Gomez-Gonzalez" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "*" "identificador" => "cor0005" ] ] ] 1 => array:2 [ "nombre" => "Mauricio" "apellidos" => "Villamizar-Villegas" ] 2 => array:2 [ "nombre" => "Hector Manuel" "apellidos" => "Zarate" ] 3 => array:2 [ "nombre" => "Juan Sebastian" "apellidos" => "Amador" ] 4 => array:2 [ "nombre" => "Celina" "apellidos" => "Gaitan-Maldonado" ] ] "afiliaciones" => array:1 [ 0 => array:2 [ "entidad" => "Central Bank of Colombia, Colombia" "identificador" => "aff0005" ] ] "correspondencia" => array:1 [ 0 => array:3 [ "identificador" => "cor0005" "etiqueta" => "⁎" "correspondencia" => "Corresponding author." ] ] ] ] "titulosAlternativos" => array:1 [ "es" => array:1 [ "titulo" => "Crédito y ciclos económicos: efectos causales en el dominio de la frecuencia" ] ] "resumenGrafico" => array:2 [ "original" => 0 "multimedia" => array:7 [ "identificador" => "fig0010" "etiqueta" => "Fig. 2" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:3 [ 0 => array:1 [ "imagen" => "gr2a.jpeg" ] 1 => array:1 [ "imagen" => "gr2b.jpeg" ] 2 => array:1 [ "imagen" => "gr2c.jpeg" ] ] "descripcion" => array:1 [ "en" => "Cross-correlations in the frequency domain between GDP and Credit by Country (Angular frequency in horizontal axis- vs. Correlation Coefficient in vertical axis). The two points marked in each graph are the maximum correlations from lagged GDP to contemporaneous Credit and vice-versa." ] ] ] "textoCompleto" => "1IntroductionThe history of economic recessions has shown that every deep downturn has been accompanied by disruptions in the financial sector. Paradoxically, up until the recent financial world crisis, the linkage between financial and macroeconomic variables remained almost entirely unexplored. Recently, a small but growing literature has surfaced regarding the importance of the financial sector and its role on the business cycle. Examples include <a class="elsevierStyleCrossRefs" href="#bib0065">Cúrdia and Woodford (2010), Christiano, Motto, and Rostagno (2010), Claessens, Kose, and Terrones (2012), Drehmann, Borio, and Tsatsaronis (2012), Gertler and Kiyotaki (2010), Hafstead and Smith (2012), Meh and Moran (2010)</a>, and <a class="elsevierStyleCrossRef" href="#bib0150">Schularick and Taylor (2012)</a>. Notwithstanding, significant effort is still needed in order to attain a comprehensive understanding of the causal links between the financial sector and the macroeconomy.In this paper we extend the work of <a class="elsevierStyleCrossRef" href="#bib0105">Gomez-Gonzalez, Ojeda-Joya, Zárate, and Tenjo-Galarza (2014)</a> to study the relationship between financial and output cycles for thirty-three countries in the frequency domain. Our sample includes both developed and emerging market economies which allow us to make several benchmark comparisons. While the literature has mainly focused on developed economies, little is known about cycle interdependence for emerging markets. Thus, our paper intends to shed some light on the latter and serve as a building block for the construction of future theoretical models.Our contributions to the literature are two-fold. First, we characterize the interdependence of credit and output cycles in the frequency domain, sidestepping the need of assumptions regarding which frequency to select in the data. Hence, in contrast to most of the existing literature, our study avoids issues that center on the differences between different time horizons of cycles (see <a class="elsevierStyleCrossRef" href="#bib0075">Drehmann et al., 2012</a>). Second, following the methodology presented in <a class="elsevierStyleCrossRef" href="#bib0040">Breitung and Candelon (2006)</a>, we perform Granger-type causality tests in the frequency domain. By doing so, we obtain stronger results than if using cross-correlation coefficients. Our paper confirms some of the previous findings from the literature but also uncovers new results. Similar to <a class="elsevierStyleCrossRef" href="#bib0035">Borio (2011)</a> and <a class="elsevierStyleCrossRef" href="#bib0075">Drehmann et al. (2012)</a>, we find that the likelihood of cycle interdependence is highest when considering medium and long-term frequencies. However, in contrast to most of the related literature, we find that Granger causality runs in both directions.2Literature review2.1Early studiesAccording to Minsky's instability hypothesis, financial cycles systematically affect real business cycles. The former is an endogenous result of when firms transition from a hedge finance scheme towards a purely speculative (or Ponzi finance) scheme. <a class="elsevierStyleCrossRef" href="#fn0005">1</a>Financial instability is then hightened during long periods of economic tranquility. In good times, the economy grows at a steady pace in which credit defaults are rare and risk-taking incentives are heightened. Examples include prolonged periods of loose monetary policy, often leading to the search for different yield strategies. As a result, firms and households’ risk tolerance increase (bearing higher levels of debt) while private banks lower their lending standards. Higher profit expectations shift the debt structure of firms towards Ponzi finance schemes, increasing investment even further. <a class="elsevierStyleCrossRef" href="#fn0010">2</a> Similarly, higher income increases households’ debt-to-income ratio.In the related literature, there is strong support of this behavior in booming periods, as seen in <a class="elsevierStyleCrossRefs" href="#bib0010">Amador, Gómez-González, and Murcia (2013), Asea and Blomberg (1998), Figueroa and Leukhina (2010), Weinberg (1995)</a>, and <a class="elsevierStyleCrossRef" href="#bib0130">Kaufmann and Scharler (2013)</a>.2.2Recent literatureIn contrast with earlier studies, the recent literature's dominant approach has consisted of modeling financial frictions; embedded in a dynamic stochastic general equilibrium (DSGE) framework. These models build on the financial accelerator model developed by <a class="elsevierStyleCrossRef" href="#bib0025">Bernanke and Gertler (1989)</a>, and extended by <a class="elsevierStyleCrossRefs" href="#bib0045">Carlstrom and Fuerst (1997), Kiyotaki and Moore (1997)</a> and <a class="elsevierStyleCrossRef" href="#bib0030">Bernanke, Gertler, and Gilchrist (1999)</a>. Overall, this strand of literature emphasizes the role of financial intermediaries and characterizes structural shocks that affect the borrowing and lending process.There are numerous renowned examples. For instance, <a class="elsevierStyleCrossRef" href="#bib0065">Cúrdia and Woodford (2010)</a> study the desirability of modifying a standard Taylor rule by incorporating variations in credit spreads and credit quantities. <a class="elsevierStyleCrossRef" href="#bib0055">Christiano et al. (2010)</a> model financial contracts and liquidity constraints by introducing financial markets and a banking sector. Similarly, <a class="elsevierStyleCrossRef" href="#bib0140">Meh and Moran (2010)</a> include a banking sector in order to solve for information problems between banks and creditors (involving differences in the level of capital). <a class="elsevierStyleCrossRef" href="#bib0095">Gertler and Kiyotaki (2010)</a> build a hybrid model based on <a class="elsevierStyleCrossRef" href="#bib0090">Gertler and Karadi (2013)</a> and <a class="elsevierStyleCrossRef" href="#bib0135">Kiyotaki and Moore (1997)</a> to allow for financial intermediation and liquidity risk. The authors show how disruptions in intermediation can induce a crisis, and how credit market interventions can help mitigate one. <a class="elsevierStyleCrossRef" href="#bib0115">Hafstead and Smith (2012)</a> develop a financial accelerator model in which a monopolistically competitive banking sector is introduced, and an interbank market exists. The authors find that shocks originating in the financial sector can have large macroeconomic effects and that monetary policy plays an important role in mitigating them.In sum, the recent growth in the literature has yielded important results vis-à-vis its critics. For instance, <a class="elsevierStyleCrossRef" href="#bib0070">Del Negro, Giannoni, and Schorfheide (2013)</a>, estimate a standard New Keynesian model with financial frictions that predicts the recent world crisis of 2009. However, it is worth noting that some authors advocate for the use of different modeling techniques. For instance, <a class="elsevierStyleCrossRef" href="#bib0035">Borio (2011)</a> recalls for a reconsideration of the prevailing paradigm embedded in macroeconomics. Particularly, he dissents from a rediscovery of the monetary nature of modern economies, in which inside credit creation plays a major role.3Frequency domain analysisThe frequency domain approach is implemented in three stages. First, we estimate the spectral function of each variable. This estimation allows comparing the shape of the spectral density with the “typical shape” identified in <a class="elsevierStyleCrossRef" href="#bib0110">Granger (1966)</a>. Second, we use the direct filter approach to extract cycles using Fourier analysis.<a class="elsevierStyleCrossRef" href="#fn0015">3</a> Finally, we estimate the co-movement between cycles by using the cross-spectral density function and its related measures of coherence.This methodology is conducted for the entire frequency range (0 to π). This approach allows estimating the proportion of the total variance determined by each periodic component. Therefore, we capture the components of credit and output by decomposing the original series and using approximation methods based on trigonometric functions at each frequency. Signal extraction techniques are based on smoothing filters (<a class="elsevierStyleCrossRef" href="#bib0050">Christiano & Fitzgerald, 2003</a>) and modeling-based procedures. However, the components of the time series give rise to spectral structures that fall within well-defined frequency bands (isolated from each other by spectral dead spaces).<a class="elsevierStyleCrossRef" href="#fn0020">4</a> Thus, the frequency domain offers a better way of implementing signal extraction methods, and filters are used to separate the components of time series.The autocovariance function of the Fourier transformation is given by the following symmetric function:<elsevierMultimedia ident="eq0005"></elsevierMultimedia>where ω is the frequency in radians in the range −π,π. The standardized function, known as the spectral density, is obtained by normalizing Eq. <a class="elsevierStyleCrossRef" href="#eq0005">(1)</a> by using γ0. A cycle is defined as a unit period of a sine or cosine function over a time interval of length 2π.It is important to note that the spectrum and the covariances are equivalent. However, some features of time series, such as serial correlation, are easier to grasp using autocovariances. Others, such as unobserved components, are easier to analyze using the whole spectrum.Values of ω near zero correspond to long-term cycles, while values of ω near π correspond to short-term cycles. The peaks observed in the spectrum indicate those periodicities which contribute the most to the variability of the series. Additionally, confidence intervals for the spectrum can be obtained from the fact that fω follows a χ2 distribution with ν degrees of freedom (where ν=3n1/22, and n stands for the sample size).The direct filter approach (DFA) emphasizes on filter errors rather than on the one-step ahead forecasting error. Furthermore, the DFA uses an algorithm based on an optimization criterion which consists of minimizing the mean square error of the filter.Given a stochastic process Yt, the real time signal extraction is concerned with the estimation of Yˆt and Y¯t such that EY¯t−Yˆt2 is minimized. In this context, Y¯t is the result of applying a symmetric filter to the original series, while Yˆt is the result of applying an asymmetric filter.The result of this minimization is a transfer function, Γω, which can be used as a filter. One particular application which we use in this study is the filter proposed by <a class="elsevierStyleCrossRef" href="#bib0050">Christiano and Fitzgerald (2003)</a>:<elsevierMultimedia ident="eq0010"></elsevierMultimedia>where bp determines the width of the pass band and bs determines the width of the stop band (see <a class="elsevierStyleCrossRef" href="#bib0160">Wildi, 2008</a>).The cross-spectral correlation function measures the correlation between two series indexed by the frequency. The square of the value of this correlation function at every frequency ω is defined as its coherence. This statistic is the analogous of the square of the correlation coefficient and takes on values in the interval [0, 1]. A value of coherence near one indicates that the two series are highly associated at the given frequency. A value near zero describes that at this frequency the series are almost independent.3.1VAR setupIn order to test for causality between GDP and Credit cycles in the frequency domain, we use the measures proposed by <a class="elsevierStyleCrossRef" href="#bib0100">Geweke (1982)</a> and <a class="elsevierStyleCrossRef" href="#bib0120">Hosoya (1991)</a> and adopted by <a class="elsevierStyleCrossRef" href="#bib0040">Breitung and Candelon (2006)</a> in a VAR system setup.Let Zt=GDPt,CREDITt be a two dimensional vector of time series observed for t=1, 2, ⋯T, which represents the total cycle of these two variables.<a class="elsevierStyleCrossRef" href="#fn0025">5</a> Thus, the VAR representation of this system can be expressed as in Eq. <a class="elsevierStyleCrossRef" href="#eq0015">(3)</a>:<elsevierMultimedia ident="eq0015"></elsevierMultimedia>The moving average (MA) representation of the system is the following:<elsevierMultimedia ident="eq0020"></elsevierMultimedia>Where<elsevierMultimedia ident="eq0025"></elsevierMultimedia>And G is the lower matrix of Cholesky decomposition. Using this representation, the spectral density of GDPt, for example, can be expressed as:<elsevierMultimedia ident="eq0030"></elsevierMultimedia>From the above expression, and following <a class="elsevierStyleCrossRef" href="#bib0040">Breitung and Candelon (2006)</a>, the measure of causality is defined as:<elsevierMultimedia ident="eq0035"></elsevierMultimedia>This causality measure is zero if Ψ12e−iω2=0, which means that the variable CREDIT does not cause GDP at frequency ω. The causality of GDP to CREDIT is built using a similar approach.4DataOur dataset (quarterly frequency) consists of credit to the private non-financial sector and real GDP for 33 economies.<a class="elsevierStyleCrossRef" href="#fn0030">6</a> Following <a class="elsevierStyleCrossRefs" href="#bib0005">Aikman, Haldane, and Nelson (2011), Jordà, Schularick, and Taylor (2011), Schularick and Taylor (2012)</a>, we use the credit cycle as a proxy for the financial cycle. In spite of a general lack of consensus on its exact definition, some studies have shown that the most parsimonious definition of the financial cycle is in terms of credit.<a class="elsevierStyleCrossRef" href="#fn0035">7</a> Most data on credit, adjusted for breaks, were obtained from the Bank for International Settlements (BIS). For countries not included in the BIS database (namely Colombia, Chile and Peru) we collected official data from each central bank. All observations on variables were collected for the longest available period, ranging from the early 1940s until the second quarter of 2013 (see <a class="elsevierStyleCrossRef" href="#tbl0005">Appendix 1</a> for a summary of descriptive statistics for each country). The nominal GDP and the Consumer Price Index (CPI) were both obtained from the International Financial Statistics database of the International Monetary Fund. All data used in this study are expressed in constant prices (using the CPI of each country as deflator).<a class="elsevierStyleCrossRef" href="#fn0040">8</a><elsevierMultimedia ident="tbl0005"></elsevierMultimedia>5Empirical results<a class="elsevierStyleCrossRefs" href="#fig0005">Figs. 1–3</a> illustrate our main results based on coherence statistics, cross-correlations in the frequency domain and Granger causality tests, respectively. In order to improve readability and to allow a comparison of our results with previous studies, we transformed the frequency domain into time intervals (quarters), using the following function:<elsevierMultimedia ident="eq0040"></elsevierMultimedia>Consequently, we followed <a class="elsevierStyleCrossRef" href="#bib0075">Drehmann et al. (2012)</a> to establish conventional duration horizons for short (5–32 quarters), medium (32–80 quarters) and long-term (more than 80 quarters) cycles.<a class="elsevierStyleCrossRef" href="#fig0005">Fig. 1</a> shows results of computing coherence statistics between credit and GDP for all 33 countries. High values of coherence correspond to a high correlation between credit and GDP. Results show that credit and GDP cycles appear to have greater correlation at medium and long-term frequencies for most countries in our sample (29 out of 33). This fact highlights the importance of looking at medium and long-term credit cycles when designing macro-prudential policies. The only exceptions include Belgium, India, Mexico and Peru, for which the greater values of coherence occur at short-term frequencies.<elsevierMultimedia ident="fig0005"></elsevierMultimedia><a class="elsevierStyleCrossRef" href="#fig0010">Fig. 2</a> depicts the cross-correlation between credit and GDP. Each sub-figure comprises the correlation between credit lags and contemporaneous GDP (negative values on the horizontal axis) as well as the correlation between GDP lags and contemporaneous credit (positive values on the horizontal axis) for the whole frequency spectrum. All countries (except Korea) exhibit global maxima at the left hand-side of the domain, indicating a positive relationship between lags in credit and contemporaneous output, specially marked in frequencies corresponding to the medium term. Conversely, all countries present a global minimum at the right hand-side of the domain. This is evidence of a negative correlation between GDP lags and contemporaneous credit, again with a higher influence over the medium term. In sum, credit cycle peaks precede booms in output, while peaks in the output cycle precede troughs in the credit cycle. This result corroborates the findings obtained by <a class="elsevierStyleCrossRef" href="#bib0150">Schularick and Taylor (2012)</a>.<elsevierMultimedia ident="fig0010"></elsevierMultimedia><a class="elsevierStyleCrossRef" href="#fig0015">Fig. 3</a> depicts results for Granger causality tests between credit and GDP, based on the methodology proposed by <a class="elsevierStyleCrossRef" href="#bib0040">Breitung and Candelon (2006)</a>. We performed this test for the 33 countries in our sample and for the two directions of causality.<a class="elsevierStyleCrossRef" href="#fn0045">9</a> Therefore, <a class="elsevierStyleCrossRef" href="#fig0015">Fig. 3</a> consists of 66 sub-figures. The horizontal line represents the critical value at the 95% significance level (values above the critical value indicate causality at a given frequency). Results show that causality runs in both directions (stronger in medium and long-term frequencies), for most casas. There is however, some heterogeneity among countries. For instance, there are countries like Peru for which causality runs in only one direction. Finally, medium and long-term causality is the most evident.<elsevierMultimedia ident="fig0015"></elsevierMultimedia>Our findings shed light on salient features of macroeconomic modeling. The relationship between financial and real variables is complex, and financial factors influence economic activity beyond exogenous shocks. Overall, we illustrate that GDP and credit cycles are not perfectly synchronized. The relationship between these two cycles is stronger when lags are included. An interesting implication for monetary policy is that it is difficult to target both financial and real variables using just one instrument. Moreover, credit should not be ignored when the objective is to stabilize the economy, as credit cycles exert important influence over the business cycle, and vice-versa.These results are similar to those found in <a class="elsevierStyleCrossRef" href="#bib0075">Drehmann et al. (2012)</a> in the sense that financial cycles are mostly related to business cycles in the medium-term. In our sample, these results hold true for developed countries such as Australia, Germany, Japan, Norway, Sweden, United Kingdom and the United States. However, while <a class="elsevierStyleCrossRef" href="#bib0075">Drehmann et al. (2012)</a> focus on the time domain, our focus lies in the frequency domain. Our study also focuses on Granger-type causality tests instead of conducting correlation analysis.6ConclusionsWe study the relationship between financial and real business cycles for thirty-three countries in the frequency domain. Our sample includes both developed and emerging market economies which allow us to make several benchmark comparisons. We contribute to the literature by first, characterizing the interdependence of credit and output cycles in the frequency domain. In contrast to most of the existing literature, our study avoids issues that center on the differences between short and long-term analysis of cycles. Second, we perform Granger-type causality tests in the frequency domain. By doing so, we obtain stronger results than studies using cross-correlation coefficients.Our paper confirms some of the previous findings from the literature but also uncovers new results. Similar to <a class="elsevierStyleCrossRef" href="#bib0035">Borio (2011)</a> and <a class="elsevierStyleCrossRef" href="#bib0075">Drehmann et al. (2012)</a>, we find that the likelihood of cycle interdependence is highest when considering medium and long-term frequencies. However, in contrast to most of the related literature, we find that Granger causality runs in both directions.As such, monetary authorities can benefit by focusing on medium and long-term credit cycles when designing macro-prudential policies. Moreover, credit cycles should be carefully analyzed when trying to stabilize the economy, as they exert important influence over the business cycle, and vice-versa. We believe that our findings elicit key structural differences between emerging and developed economies that can potentially serve as building blocks for the construction of future theoretical models.Conflict of interestsThe authors declare no conflict of interest." "textoCompletoSecciones" => array:1 [ "secciones" => array:14 [ 0 => array:3 [ "identificador" => "xres817503" "titulo" => "Abstract" "secciones" => array:1 [ 0 => array:1 [ "identificador" => "abst0005" ] ] ] 1 => array:2 [ "identificador" => "xpalclavsec814690" "titulo" => "Keywords" ] 2 => array:2 [ "identificador" => "xpalclavsec814689" "titulo" => "JEL classification" ] 3 => array:3 [ "identificador" => "xres817502" "titulo" => "Resumen" "secciones" => array:1 [ 0 => array:1 [ "identificador" => "abst0010" ] ] ] 4 => array:2 [ "identificador" => "xpalclavsec814691" "titulo" => "Palabras clave" ] 5 => array:2 [ "identificador" => "xpalclavsec814692" "titulo" => "Códigos JEL" ] 6 => array:2 [ "identificador" => "sec0005" "titulo" => "Introduction" ] 7 => array:3 [ "identificador" => "sec0010" "titulo" => "Literature review" "secciones" => array:2 [ 0 => array:2 [ "identificador" => "sec0015" "titulo" => "Early studies" ] 1 => array:2 [ "identificador" => "sec0020" "titulo" => "Recent literature" ] ] ] 8 => array:3 [ "identificador" => "sec0025" "titulo" => "Frequency domain analysis" "secciones" => array:1 [ 0 => array:2 [ "identificador" => "sec0030" "titulo" => "VAR setup" ] ] ] 9 => array:2 [ "identificador" => "sec0035" "titulo" => "Data" ] 10 => array:2 [ "identificador" => "sec0040" "titulo" => "Empirical results" ] 11 => array:2 [ "identificador" => "sec0045" "titulo" => "Conclusions" ] 12 => array:2 [ "identificador" => "sec0050" "titulo" => "Conflict of interests" ] 13 => array:1 [ "titulo" => "References" ] ] ] "pdfFichero" => "main.pdf" "tienePdf" => true "fechaRecibido" => "2015-02-09" "fechaAceptado" => "2015-05-15" "PalabrasClave" => array:2 [ "en" => array:2 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Keywords" "identificador" => "xpalclavsec814690" "palabras" => array:3 [ 0 => "Frequency domain" 1 => "Granger causality" 2 => "Credit and output cycle interdependence" ] ] 1 => array:4 [ "clase" => "jel" "titulo" => "JEL classification" "identificador" => "xpalclavsec814689" "palabras" => array:3 [ 0 => "E32" 1 => "E44" 2 => "C38" ] ] ] "es" => array:2 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Palabras clave" "identificador" => "xpalclavsec814691" "palabras" => array:3 [ 0 => "Dominio de la frecuencia" 1 => "Causalidad de Granger" 2 => "Interdependencia entre ciclos financieros y reales" ] ] 1 => array:4 [ "clase" => "jel" "titulo" => "Códigos JEL" "identificador" => "xpalclavsec814692" "palabras" => array:3 [ 0 => "E32" 1 => "E44" 2 => "C38" ] ] ] ] "tieneResumen" => true "resumen" => array:2 [ "en" => array:2 [ "titulo" => "Abstract" "resumen" => "The history of economic recessions has shown that every deep downturn has been accompanied by disruptions in the financial sector. Paradoxically, up until the financial world crisis of 2007–2009, little attention was given to macroeconomic and financial interdependence. In this paper, a study is conducted on the relationship between financial and real business cycles for a sample of thirty-three countries in the frequency domain. Specifically, the features of the interdependence of credit and output cycles are analysed and Granger-type causality tests are carried out in the frequency domain. The main findings of the study indicate that the likelihood of cycle interdependence is highest when considering medium and long-term frequencies, and that Granger causality runs in both directions." ] "es" => array:2 [ "titulo" => "Resumen" "resumen" => "Tradicionalmente, las recesiones han venido acompañadas de disrupciones importantes en el funcionamiento del sistema financiero. Paradójicamente, antes de la crisis financiera de 2007-2009 se daba muy poca importancia a la interdependencia entre la macroeconomía y el sistema financiero. En este documento estudiamos la relación entre ciclos financieros y ciclos de producto para una muestra de treinta y tres países. Específicamente, caracterizamos la interdependencia entre estos ciclos y realizamos pruebas de causalidad tipo Granger en el dominio de la frecuencia. Nuestros resultados principales indican que la mayor interdependencia ocurre en frecuencias asociables al mediano y largo plazo. Encontramos evidencia de causalidad bi-direccional." ] ] "NotaPie" => array:10 [ 0 => array:2 [ "etiqueta" => "☆" "nota" => "The findings, recommendations, interpretations and conclusions expressed in this paper are those of the authors and not necessarily reflect the view of the Banco de la Republica nor its Board of Directors." ] 1 => array:3 [ "etiqueta" => "1" "nota" => "Minsky identified three different types of debt structures under which firms can be categorized: (1) hedge finance, (2) speculative finance, and (3) Ponzi finance. The first corresponds to when cash flows are sufficient for paying interest and principal debt payments. The second corresponds to paying interest payments (but not for repaying the principal). Thus, firms under this category need to issue new debt. Finally, the third correspond to when cash flows are not enough to even cover interest payments. Firms under this last category are in continuous need to refinance their debts, and are extremely vulnerable to changes in current and future short-term interest rates." "identificador" => "fn0005" ] 2 => array:3 [ "etiqueta" => "2" "nota" => "Minsky argues that, under monetary tightening, speculative firms will turn into Ponzi, and the net worth of firms that were already in Ponzi positions will decrease and will have to compensate for cash flow shortfalls by selling liquid assets. If fire-sales are sufficiently large, market values may collapse, increasing the likelihood of a debt-deflationary process." "identificador" => "fn0010" ] 3 => array:3 [ "etiqueta" => "3" "nota" => "Granger-type causality tests using filtered data can potentially corrupt empirical estimates (see <a class="elsevierStyleCrossRefs" href="#bib0020">Barnett & Seth, 2011; Florin, Gross, Pfeifer, Fink, & Timmermann, 2010; Geweke, 1982</a>). In essence these studies show that filtering is inappropriate for isolating causal influences within specific frequency bands. However, we believe that this limitation falls outside the scope of our study and should be analyzed in future research, given its importance." "identificador" => "fn0015" ] 4 => array:3 [ "etiqueta" => "4" "nota" => "See <a class="elsevierStyleCrossRef" href="#bib0145">Pollock (2000)</a>." "identificador" => "fn0020" ] 5 => array:3 [ "etiqueta" => "5" "nota" => "Variables in Zt correspond to the stationary cyclical component of the series. Hence, cointegration analysis does not apply." "identificador" => "fn0025" ] 6 => array:3 [ "etiqueta" => "6" "nota" => "Australia, Austria, Belgium, Brazil, Canada, Chile, Colombia, Czech Republic, Denmark, Finland, France, Germany, Hungary, India, Indonesia, Ireland, Italy, Japan, South Korea, Mexico, New Zealand, Norway, Peru, Poland, Portugal, Russia, South Africa, Spain, Sweden, Switzerland, Turkey, United Kingdom and United States." "identificador" => "fn0030" ] 7 => array:3 [ "etiqueta" => "7" "nota" => "See, for instance, <a class="elsevierStyleCrossRef" href="#bib0035">Borio (2011)</a>." "identificador" => "fn0035" ] 8 => array:3 [ "etiqueta" => "8" "nota" => "For comparability purposes we use the CPI instead of the GDP deflator to express variables in real terms. Studies such as <a class="elsevierStyleCrossRef" href="#bib0075">Drehmann et al. (2012)</a> and <a class="elsevierStyleCrossRef" href="#bib0105">Gomez-Gonzalez, Ojeda-Joya, Zárate, & Tenjo-Galarza (2014)</a> also use the CPI as the price deflator." "identificador" => "fn0040" ] 9 => array:3 [ "etiqueta" => "9" "nota" => "All data were de-trended before performing these tests." "identificador" => "fn0045" ] ] "multimedia" => array:12 [ 0 => array:7 [ "identificador" => "fig0005" "etiqueta" => "Fig. 1" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:3 [ 0 => array:1 [ "imagen" => "gr1a.jpeg" ] 1 => array:1 [ "imagen" => "gr1b.jpeg" ] 2 => array:1 [ "imagen" => "gr1c.jpeg" ] ] "descripcion" => array:1 [ "en" => "Coherence Statistics between GDP and Credit by Country (Frequency Domain – Horizontal axis measured in quarters)." ] ] 1 => array:7 [ "identificador" => "fig0010" "etiqueta" => "Fig. 2" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:3 [ 0 => array:1 [ "imagen" => "gr2a.jpeg" ] 1 => array:1 [ "imagen" => "gr2b.jpeg" ] 2 => array:1 [ "imagen" => "gr2c.jpeg" ] ] "descripcion" => array:1 [ "en" => "Cross-correlations in the frequency domain between GDP and Credit by Country (Angular frequency in horizontal axis- vs. Correlation Coefficient in vertical axis). The two points marked in each graph are the maximum correlations from lagged GDP to contemporaneous Credit and vice-versa." ] ] 2 => array:7 [ "identificador" => "fig0015" "etiqueta" => "Fig. 3" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:4 [ 0 => array:1 [ "imagen" => "gr3a.jpeg" ] 1 => array:1 [ "imagen" => "gr3b.jpeg" ] 2 => array:1 [ "imagen" => "gr3c.jpeg" ] 3 => array:1 [ "imagen" => "gr3d.jpeg" ] ] "descripcion" => array:1 [ "en" => "Causality Tests by Country in the Frequency Domain (Quarters in the horizontal axis vs. Statistic of Granger's Causality Test in the vertical axis). The horizontal line in each of the graphs represents the critical values for the Granger's causality test. Angular frequency values above it suggest that variables in these frequencies cause one another." ] ] 3 => array:7 [ "identificador" => "tbl0005" "etiqueta" => "Appendix 1" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "tabla" => array:2 [ "leyenda" => "Source: Authors’ calculations. P25 and P75 correspond to the bottom 25% and top 75% values, respectively." "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="table-head " align="left" valign="top" scope="col">Country \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col">Time Period \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " colspan="3" align="left" valign="top" scope="col" style="border-bottom: 2px solid black">GDP</th><th class="td" title="table-head " colspan="3" align="left" valign="top" scope="col" style="border-bottom: 2px solid black">CREDIT</th></tr><tr title="table-row"><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black"> \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black"> \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">P25 \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">MEDIAN \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">P75 \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">P25 \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">MEDIAN \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">P75 \t\t\t\t\t\t\n \t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Australia \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Jun-59–Jun-13 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">2.31 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">3.58 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">4.98 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">3.57 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">6.44 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">9.32 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Austria \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Jun-59–Jun-13 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1.42 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">2.83 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">4.39 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">3.33 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">6.53 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">10.89 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Belgium \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Jan-73–Jun-13 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.85 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">2.07 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">2.96 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">2.96 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">5.78 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">8.91 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Brazil \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Jan-96–Jun-13 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.86 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">2.77 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">4.90 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.59 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">9.32 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">16.87 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Canada \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Jun-59–Jun-13 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1.99 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">3.61 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">5.09 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">2.69 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">5.25 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">8.80 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Chile \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Jun-82–Jun-13 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">3.44 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">5.53 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">7.26 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">−3.41 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1.40 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">5.82 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Colombia \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Jun-79–Jun-13 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">2.34 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">3.87 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">5.44 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">−15.99 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">−9.46 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">−4.86 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Czech Republic \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Jun-95–Jun-13 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.38 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">3.19 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">5.23 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">−1.48 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">3.69 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">9.02 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Denmark \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Jun-59–Jun-13 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.01 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1.75 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">3.08 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1.75 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">5.26 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">9.11 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Finland \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Jan-73–Jun-13 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1.29 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">3.07 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">4.92 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">2.29 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">5.54 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">8.13 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">France \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Jan-72–Jun-13 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1.11 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">2.07 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">3.30 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1.55 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">3.58 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">6.83 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Germany \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Jun-59–Jun-13 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.87 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">2.58 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">4.18 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">2.16 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">3.99 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">7.12 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Hungary \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Jan-92–Jun-13 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.35 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">3.04 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">4.16 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">−5.28 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">8.53 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">15.41 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Iceland \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Jun-06–Jun-13 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.44 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">2.53 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">6.24 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">−17.09 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">29.12 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">39.03 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">India \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Jun-59–Jun-13 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">5.26 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">6.66 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">8.95 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">3.20 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">6.91 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">11.87 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Indonesia \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Sep-78–Jun-13 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">4.26 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">5.42 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">6.14 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">3.07 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">11.92 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">19.10 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Ireland \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Sep-73–Jun-13 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.35 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">4.61 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">6.85 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">2.35 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">7.79 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">13.84 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Italy \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Jun-59–Jun-13 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.39 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1.60 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">2.76 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1.13 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">6.61 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">9.62 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Japan \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Jan-67–Jun-13 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1.38 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">4.07 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">7.67 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">−0.83 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">3.65 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">9.20 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Korea \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Jun-72–Jun-13 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">4.32 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">6.93 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">10.30 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">6.36 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">11.98 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">16.35 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Luxembourg \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Jun-05–Jun-13 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1.55 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">3.17 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">6.40 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1.52 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">9.04 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">29.93 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Mexico \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Jan-83–Jun-13 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1.06 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">3.17 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">4.71 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">−9.09 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">4.67 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">16.77 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">New Zealand \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Jun-90–Jun-13 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1.14 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">2.97 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">4.33 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">2.87 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">6.60 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">9.35 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Norway \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Jan-56–Jun-13 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1.24 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">3.04 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">4.92 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">2.34 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">5.01 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">8.60 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Peru \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Jun-96–Jun-13 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1.78 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">5.40 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">7.51 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">−5.07 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">5.82 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">16.42 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Poland \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Jun-94–Jun-13 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">2.40 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">4.33 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">5.84 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1.18 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">11.16 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">22.67 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Portugal \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Jun-59–Jun-13 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.72 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">2.27 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">4.10 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1.43 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">6.91 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">11.02 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Russia \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Sep-97–Jun-13 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1.50 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">4.86 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">8.03 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">2.69 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">17.58 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">25.73 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Slovenia \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Jun-09–Jun-13 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">2.05 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">3.79 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">5.33 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.04 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">4.55 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">16.43 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">South Africa \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Jun-67–Jun-13 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1.63 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">3.46 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">5.26 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.65 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">5.15 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">9.22 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Spain \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Jun-72–Jun-13 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1.05 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">2.79 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">4.07 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.88 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">5.85 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">12.26 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Sweden \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Jun-63–Jun-13 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.71 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">2.69 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">3.92 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1.32 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">3.99 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">9.25 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Switzerland \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Sep-77–Jun-13 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.48 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">2.28 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">3.46 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.81 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">3.17 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">6.14 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Turkey \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Jun-88–Jun-13 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1.38 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">5.41 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">8.07 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">−3.63 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">8.94 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">19.87 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">United Kingdom \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Jun-90–Jun-13 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1.51 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">2.51 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">3.89 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1.25 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">5.98 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">9.05 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">United States \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Jun-59–Jun-13 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1.85 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">3.16 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">4.82 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">2.41 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">5.00 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">7.09 \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab1374221.png" ] ] ] ] "descripcion" => array:1 [ "en" => "Summary statistics." ] ] 4 => array:6 [ "identificador" => "eq0005" "etiqueta" => "(1)" "tipo" => "MULTIMEDIAFORMULA" "mostrarFloat" => false "mostrarDisplay" => true "Formula" => array:5 [ "Matematica" => "fω=12πγ0+2∑τ=1∞γτcosωτ" "Fichero" => "STRIPIN_si1.jpeg" "Tamanyo" => 3245 "Alto" => 49 "Ancho" => 281 ] ] 5 => array:6 [ "identificador" => "eq0010" "etiqueta" => "(2)" "tipo" => "MULTIMEDIAFORMULA" "mostrarFloat" => false "mostrarDisplay" => true "Formula" => array:5 [ "Matematica" => "Γω=1,if0≤|ω|≤πbpπbp−|ω|πbs−πbpifπbp<|ω|≤πbs0πbs<|ω|≤π" "Fichero" => "STRIPIN_si11.jpeg" "Tamanyo" => 5141 "Alto" => 82 "Ancho" => 294 ] ] 6 => array:6 [ "identificador" => "eq0015" "etiqueta" => "(3)" "tipo" => "MULTIMEDIAFORMULA" "mostrarFloat" => false "mostrarDisplay" => true "Formula" => array:5 [ "Matematica" => "ΘLZt=ϵt" "Fichero" => "STRIPIN_si13.jpeg" "Tamanyo" => 848 "Alto" => 16 "Ancho" => 84 ] ] 7 => array:6 [ "identificador" => "eq0020" "etiqueta" => "(4)" "tipo" => "MULTIMEDIAFORMULA" "mostrarFloat" => false "mostrarDisplay" => true "Formula" => array:5 [ "Matematica" => "Zt=ΦLϵt=Φ11LΦ12LΦ21LΦ22Lϵ1tϵ2t=ΨLηt=Ψ11LΨ12LΨ21LΨ22Lη1tη2t" "Fichero" => "STRIPIN_si14.jpeg" "Tamanyo" => 7656 "Alto" => 118 "Ancho" => 301 ] ] 8 => array:5 [ "identificador" => "eq0025" "tipo" => "MULTIMEDIAFORMULA" "mostrarFloat" => false "mostrarDisplay" => true "Formula" => array:5 [ "Matematica" => "ΦL=ΦL−1andΨL=ΦLG−1" "Fichero" => "STRIPIN_si15.jpeg" "Tamanyo" => 2357 "Alto" => 18 "Ancho" => 246 ] ] 9 => array:6 [ "identificador" => "eq0030" "etiqueta" => "(5)" "tipo" => "MULTIMEDIAFORMULA" "mostrarFloat" => false "mostrarDisplay" => true "Formula" => array:5 [ "Matematica" => "fGDPω=12πΨ11e−iω2+Ψ12e−iω2" "Fichero" => "STRIPIN_si16.jpeg" "Tamanyo" => 3126 "Alto" => 32 "Ancho" => 316 ] ] 10 => array:6 [ "identificador" => "eq0035" "etiqueta" => "(6)" "tipo" => "MULTIMEDIAFORMULA" "mostrarFloat" => false "mostrarDisplay" => true "Formula" => array:5 [ "Matematica" => "MCREDIT⟶GDP=log1+|Ψ12e−iω|2|Ψ11e−iω|2" "Fichero" => "STRIPIN_si17.jpeg" "Tamanyo" => 3639 "Alto" => 49 "Ancho" => 269 ] ] 11 => array:6 [ "identificador" => "eq0040" "etiqueta" => "(7)" "tipo" => "MULTIMEDIAFORMULA" "mostrarFloat" => false "mostrarDisplay" => true "Formula" => array:5 [ "Matematica" => "Time=2πAngular frequency*4quarters" "Fichero" => "STRIPIN_si19.jpeg" "Tamanyo" => 2899 "Alto" => 34 "Ancho" => 273 ] ] ] "bibliografia" => array:2 [ "titulo" => "References" "seccion" => array:1 [ 0 => array:2 [ "identificador" => "bibs0005" "bibliografiaReferencia" => array:32 [ 0 => array:3 [ "identificador" => "bib0005" "etiqueta" => "[Aikman et al., 2011]" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "“Curbing the Credit Cycle,” March" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:3 [ 0 => "D. 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Crédito y ciclos económicos: efectos causales en el dominio de la frecuencia

Jose Eduardo Gomez-Gonzalez*, Mauricio Villamizar-Villegas, Hector Manuel Zarate, Juan Sebastian Amador, Celina Gaitan-Maldonado

Central Bank of Colombia, Colombia

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    "textoCompleto" => "<span class="elsevierStyleSections"><span id="sec0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">1</span><span class="elsevierStyleSectionTitle" id="sect0035">Introduction</span><p id="p0005" class="elsevierStylePara elsevierViewall">The history of economic recessions has shown that every deep downturn has been accompanied by disruptions in the financial sector&#46; Paradoxically&#44; up until the recent financial world crisis&#44; the linkage between financial and macroeconomic variables remained almost entirely unexplored&#46; Recently&#44; a small but growing literature has surfaced regarding the importance of the financial sector and its role on the business cycle&#46; Examples include <a class="elsevierStyleCrossRefs" href="#bib0065">C&#250;rdia and Woodford &#40;2010&#41;&#44; Christiano&#44; Motto&#44; and Rostagno &#40;2010&#41;&#44; Claessens&#44; Kose&#44; and Terrones &#40;2012&#41;&#44; Drehmann&#44; Borio&#44; and Tsatsaronis &#40;2012&#41;&#44; Gertler and Kiyotaki &#40;2010&#41;&#44; Hafstead and Smith &#40;2012&#41;&#44; Meh and Moran &#40;2010&#41;</a>&#44; and <a class="elsevierStyleCrossRef" href="#bib0150">Schularick and Taylor &#40;2012&#41;</a>&#46; Notwithstanding&#44; significant effort is still needed in order to attain a comprehensive understanding of the causal links between the financial sector and the macroeconomy&#46;</p><p id="p0010" class="elsevierStylePara elsevierViewall">In this paper we extend the work of <a class="elsevierStyleCrossRef" href="#bib0105">Gomez-Gonzalez&#44; Ojeda-Joya&#44; Z&#225;rate&#44; and Tenjo-Galarza &#40;2014&#41;</a> to study the relationship between financial and output cycles for thirty-three countries in the frequency domain&#46; Our sample includes both developed and emerging market economies which allow us to make several benchmark comparisons&#46; While the literature has mainly focused on developed economies&#44; little is known about cycle interdependence for emerging markets&#46; Thus&#44; our paper intends to shed some light on the latter and serve as a building block for the construction of future theoretical models&#46;</p><p id="p0015" class="elsevierStylePara elsevierViewall">Our contributions to the literature are two-fold&#46; First&#44; we characterize the interdependence of credit and output cycles in the frequency domain&#44; sidestepping the need of assumptions regarding which frequency to select in the data&#46; Hence&#44; in contrast to most of the existing literature&#44; our study avoids issues that center on the differences between different time horizons of cycles &#40;see <a class="elsevierStyleCrossRef" href="#bib0075">Drehmann et al&#46;&#44; 2012</a>&#41;&#46; Second&#44; following the methodology presented in <a class="elsevierStyleCrossRef" href="#bib0040">Breitung and Candelon &#40;2006&#41;</a>&#44; we perform Granger-type causality tests in the frequency domain&#46; By doing so&#44; we obtain stronger results than if using cross-correlation coefficients&#46; Our paper confirms some of the previous findings from the literature but also uncovers new results&#46; Similar to <a class="elsevierStyleCrossRef" href="#bib0035">Borio &#40;2011&#41;</a> and <a class="elsevierStyleCrossRef" href="#bib0075">Drehmann et al&#46; &#40;2012&#41;</a>&#44; we find that the likelihood of cycle interdependence is highest when considering medium and long-term frequencies&#46; However&#44; in contrast to most of the related literature&#44; we find that Granger causality runs in both directions&#46;</p></span><span id="sec0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">2</span><span class="elsevierStyleSectionTitle" id="sect0040">Literature review</span><span id="sec0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">2&#46;1</span><span class="elsevierStyleSectionTitle" id="sect0045">Early studies</span><p id="p0020" class="elsevierStylePara elsevierViewall">According to Minsky&#39;s instability hypothesis&#44; financial cycles systematically affect real business cycles&#46; The former is an endogenous result of when firms transition from a hedge finance scheme towards a purely speculative &#40;or Ponzi finance&#41; scheme&#46; <a class="elsevierStyleCrossRef" href="#fn0005"><span class="elsevierStyleSup">1</span></a></p><p id="p0025" class="elsevierStylePara elsevierViewall">Financial instability is then hightened during long periods of economic tranquility&#46; In good times&#44; the economy grows at a steady pace in which credit defaults are rare and risk-taking incentives are heightened&#46; Examples include prolonged periods of loose monetary policy&#44; often leading to the search for different yield strategies&#46; As a result&#44; firms and households&#8217; risk tolerance increase &#40;bearing higher levels of debt&#41; while private banks lower their lending standards&#46; Higher profit expectations shift the debt structure of firms towards Ponzi finance schemes&#44; increasing investment even further&#46; <a class="elsevierStyleCrossRef" href="#fn0010"><span class="elsevierStyleSup">2</span></a> Similarly&#44; higher income increases households&#8217; debt-to-income ratio&#46;</p><p id="p0030" class="elsevierStylePara elsevierViewall">In the related literature&#44; there is strong support of this behavior in booming periods&#44; as seen in <a class="elsevierStyleCrossRefs" href="#bib0010">Amador&#44; G&#243;mez-Gonz&#225;lez&#44; and Murcia &#40;2013&#41;&#44; Asea and Blomberg &#40;1998&#41;&#44; Figueroa and Leukhina &#40;2010&#41;&#44; Weinberg &#40;1995&#41;</a>&#44; and <a class="elsevierStyleCrossRef" href="#bib0130">Kaufmann and Scharler &#40;2013&#41;</a>&#46;</p></span><span id="sec0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">2&#46;2</span><span class="elsevierStyleSectionTitle" id="sect0050">Recent literature</span><p id="p0035" class="elsevierStylePara elsevierViewall">In contrast with earlier studies&#44; the recent literature&#39;s dominant approach has consisted of modeling financial frictions&#59; embedded in a dynamic stochastic general equilibrium &#40;DSGE&#41; framework&#46; These models build on the financial accelerator model developed by <a class="elsevierStyleCrossRef" href="#bib0025">Bernanke and Gertler &#40;1989&#41;</a>&#44; and extended by <a class="elsevierStyleCrossRefs" href="#bib0045">Carlstrom and Fuerst &#40;1997&#41;&#44; Kiyotaki and Moore &#40;1997&#41;</a> and <a class="elsevierStyleCrossRef" href="#bib0030">Bernanke&#44; Gertler&#44; and Gilchrist &#40;1999&#41;</a>&#46; Overall&#44; this strand of literature emphasizes the role of financial intermediaries and characterizes structural shocks that affect the borrowing and lending process&#46;</p><p id="p0040" class="elsevierStylePara elsevierViewall">There are numerous renowned examples&#46; For instance&#44; <a class="elsevierStyleCrossRef" href="#bib0065">C&#250;rdia and Woodford &#40;2010&#41;</a> study the desirability of modifying a standard Taylor rule by incorporating variations in credit spreads and credit quantities&#46; <a class="elsevierStyleCrossRef" href="#bib0055">Christiano et al&#46; &#40;2010&#41;</a> model financial contracts and liquidity constraints by introducing financial markets and a banking sector&#46; Similarly&#44; <a class="elsevierStyleCrossRef" href="#bib0140">Meh and Moran &#40;2010&#41;</a> include a banking sector in order to solve for information problems between banks and creditors &#40;involving differences in the level of capital&#41;&#46; <a class="elsevierStyleCrossRef" href="#bib0095">Gertler and Kiyotaki &#40;2010&#41;</a> build a hybrid model based on <a class="elsevierStyleCrossRef" href="#bib0090">Gertler and Karadi &#40;2013&#41;</a> and <a class="elsevierStyleCrossRef" href="#bib0135">Kiyotaki and Moore &#40;1997&#41;</a> to allow for financial intermediation and liquidity risk&#46; The authors show how disruptions in intermediation can induce a crisis&#44; and how credit market interventions can help mitigate one&#46; <a class="elsevierStyleCrossRef" href="#bib0115">Hafstead and Smith &#40;2012&#41;</a> develop a financial accelerator model in which a monopolistically competitive banking sector is introduced&#44; and an interbank market exists&#46; The authors find that shocks originating in the financial sector can have large macroeconomic effects and that monetary policy plays an important role in mitigating them&#46;</p><p id="p0045" class="elsevierStylePara elsevierViewall">In sum&#44; the recent growth in the literature has yielded important results vis-&#224;-vis its critics&#46; For instance&#44; <a class="elsevierStyleCrossRef" href="#bib0070">Del Negro&#44; Giannoni&#44; and Schorfheide &#40;2013&#41;</a>&#44; estimate a standard New Keynesian model with financial frictions that predicts the recent world crisis of 2009&#46; However&#44; it is worth noting that some authors advocate for the use of different modeling techniques&#46; For instance&#44; <a class="elsevierStyleCrossRef" href="#bib0035">Borio &#40;2011&#41;</a> recalls for a reconsideration of the prevailing paradigm embedded in macroeconomics&#46; Particularly&#44; he dissents from a rediscovery of the monetary nature of modern economies&#44; in which inside credit creation plays a major role&#46;</p></span></span><span id="sec0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">3</span><span class="elsevierStyleSectionTitle" id="sect0055">Frequency domain analysis</span><p id="p0050" class="elsevierStylePara elsevierViewall">The frequency domain approach is implemented in three stages&#46; First&#44; we estimate the spectral function of each variable&#46; This estimation allows comparing the shape of the spectral density with the &#8220;typical shape&#8221; identified in <a class="elsevierStyleCrossRef" href="#bib0110">Granger &#40;1966&#41;</a>&#46; Second&#44; we use the direct filter approach to extract cycles using Fourier analysis&#46;<a class="elsevierStyleCrossRef" href="#fn0015"><span class="elsevierStyleSup">3</span></a> Finally&#44; we estimate the co-movement between cycles by using the cross-spectral density function and its related measures of coherence&#46;</p><p id="p0055" class="elsevierStylePara elsevierViewall">This methodology is conducted for the entire frequency range &#40;0 to <span class="elsevierStyleItalic">&#960;</span>&#41;&#46; This approach allows estimating the proportion of the total variance determined by each periodic component&#46; Therefore&#44; we capture the components of credit and output by decomposing the original series and using approximation methods based on trigonometric functions at each frequency&#46; Signal extraction techniques are based on smoothing filters &#40;<a class="elsevierStyleCrossRef" href="#bib0050">Christiano &#38; Fitzgerald&#44; 2003</a>&#41; and modeling-based procedures&#46; However&#44; the components of the time series give rise to spectral structures that fall within well-defined frequency bands &#40;isolated from each other by spectral dead spaces&#41;&#46;<a class="elsevierStyleCrossRef" href="#fn0020"><span class="elsevierStyleSup">4</span></a> Thus&#44; the frequency domain offers a better way of implementing signal extraction methods&#44; and filters are used to separate the components of time series&#46;</p><p id="p0060" class="elsevierStylePara elsevierViewall">The autocovariance function of the Fourier transformation is given by the following symmetric function&#58;</p><p id="p0065" class="elsevierStylePara elsevierViewall"><elsevierMultimedia ident="eq0005"></elsevierMultimedia>where <span class="elsevierStyleItalic">&#969;</span> is the frequency in radians in the range &#8722;&#960;&#44;&#960;&#46; The standardized function&#44; known as the spectral density&#44; is obtained by normalizing Eq&#46; <a class="elsevierStyleCrossRef" href="#eq0005">&#40;1&#41;</a> by using &#947;0&#46; A cycle is defined as a unit period of a sine or cosine function over a time interval of length 2<span class="elsevierStyleItalic">&#960;</span><span class="elsevierStyleHsp" style=""></span>&#46;</p><p id="p0070" class="elsevierStylePara elsevierViewall">It is important to note that the spectrum and the covariances are equivalent&#46; However&#44; some features of time series&#44; such as serial correlation&#44; are easier to grasp using autocovariances&#46; Others&#44; such as unobserved components&#44; are easier to analyze using the whole spectrum&#46;</p><p id="p0075" class="elsevierStylePara elsevierViewall">Values of <span class="elsevierStyleItalic">&#969;</span> near zero correspond to long-term cycles&#44; while values of <span class="elsevierStyleItalic">&#969;</span> near <span class="elsevierStyleItalic">&#960;</span> correspond to short-term cycles&#46; The peaks observed in the spectrum indicate those periodicities which contribute the most to the variability of the series&#46; Additionally&#44; confidence intervals for the spectrum can be obtained from the fact that f&#969; follows a <span class="elsevierStyleItalic">&#967;</span><span class="elsevierStyleSup">2</span> distribution with <span class="elsevierStyleItalic">&#957;</span> degrees of freedom &#40;where &#957;&#61;3n1&#47;22&#44; and <span class="elsevierStyleItalic">n</span> stands for the sample size&#41;&#46;</p><p id="p0080" class="elsevierStylePara elsevierViewall">The direct filter approach &#40;DFA&#41; emphasizes on filter errors rather than on the one-step ahead forecasting error&#46; Furthermore&#44; the DFA uses an algorithm based on an optimization criterion which consists of minimizing the mean square error of the filter&#46;</p><p id="p0085" class="elsevierStylePara elsevierViewall">Given a stochastic process Yt&#44; the real time signal extraction is concerned with the estimation of Y&#710;t and Y&#175;t such that EY&#175;t&#8722;Y&#710;t2 is minimized&#46; In this context&#44; Y&#175;t is the result of applying a symmetric filter to the original series&#44; while Y&#710;t is the result of applying an asymmetric filter&#46;</p><p id="p0090" class="elsevierStylePara elsevierViewall">The result of this minimization is a transfer function&#44; &#915;&#969;&#44; which can be used as a filter&#46; One particular application which we use in this study is the filter proposed by <a class="elsevierStyleCrossRef" href="#bib0050">Christiano and Fitzgerald &#40;2003&#41;</a>&#58;</p><p id="p0095" class="elsevierStylePara elsevierViewall"><elsevierMultimedia ident="eq0010"></elsevierMultimedia>where <span class="elsevierStyleItalic">b</span><span class="elsevierStyleInf"><span class="elsevierStyleItalic">p</span></span> determines the width of the pass band and <span class="elsevierStyleItalic">b</span><span class="elsevierStyleInf"><span class="elsevierStyleItalic">s</span></span> determines the width of the stop band &#40;see <a class="elsevierStyleCrossRef" href="#bib0160">Wildi&#44; 2008</a>&#41;&#46;</p><p id="p0100" class="elsevierStylePara elsevierViewall">The cross-spectral correlation function measures the correlation between two series indexed by the frequency&#46; The square of the value of this correlation function at every frequency <span class="elsevierStyleItalic">&#969;</span> is defined as its coherence&#46; This statistic is the analogous of the square of the correlation coefficient and takes on values in the interval &#91;0&#44; 1&#93;&#46; A value of coherence near one indicates that the two series are highly associated at the given frequency&#46; A value near zero describes that at this frequency the series are almost independent&#46;</p><span id="sec0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">3&#46;1</span><span class="elsevierStyleSectionTitle" id="sect0060">VAR setup</span><p id="p0105" class="elsevierStylePara elsevierViewall">In order to test for causality between GDP and Credit cycles in the frequency domain&#44; we use the measures proposed by <a class="elsevierStyleCrossRef" href="#bib0100">Geweke &#40;1982&#41;</a> and <a class="elsevierStyleCrossRef" href="#bib0120">Hosoya &#40;1991&#41;</a> and adopted by <a class="elsevierStyleCrossRef" href="#bib0040">Breitung and Candelon &#40;2006&#41;</a> in a VAR system setup&#46;</p><p id="p0110" class="elsevierStylePara elsevierViewall">Let Zt&#61;GDPt&#44;CREDITt be a two dimensional vector of time series observed for <span class="elsevierStyleItalic">t</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>1&#44; 2&#44; &#8943;<span class="elsevierStyleHsp" style=""></span><span class="elsevierStyleItalic">T</span>&#44; which represents the total cycle of these two variables&#46;<a class="elsevierStyleCrossRef" href="#fn0025"><span class="elsevierStyleSup">5</span></a> Thus&#44; the VAR representation of this system can be expressed as in Eq&#46; <a class="elsevierStyleCrossRef" href="#eq0015">&#40;3&#41;</a>&#58;</p><p id="p0115" class="elsevierStylePara elsevierViewall"><elsevierMultimedia ident="eq0015"></elsevierMultimedia></p><p id="p0120" class="elsevierStylePara elsevierViewall">The moving average &#40;MA&#41; representation of the system is the following&#58;</p><p id="p0125" class="elsevierStylePara elsevierViewall"><elsevierMultimedia ident="eq0020"></elsevierMultimedia>Where</p><p id="p0130" class="elsevierStylePara elsevierViewall"><elsevierMultimedia ident="eq0025"></elsevierMultimedia>And <span class="elsevierStyleItalic">G</span> is the lower matrix of Cholesky decomposition&#46; Using this representation&#44; the spectral density of <span class="elsevierStyleItalic">GDP</span><span class="elsevierStyleInf"><span class="elsevierStyleItalic">t</span></span>&#44; for example&#44; can be expressed as&#58;</p><p id="p0135" class="elsevierStylePara elsevierViewall"><elsevierMultimedia ident="eq0030"></elsevierMultimedia></p><p id="p0140" class="elsevierStylePara elsevierViewall">From the above expression&#44; and following <a class="elsevierStyleCrossRef" href="#bib0040">Breitung and Candelon &#40;2006&#41;</a>&#44; the measure of causality is defined as&#58;</p><p id="p0145" class="elsevierStylePara elsevierViewall"><elsevierMultimedia ident="eq0035"></elsevierMultimedia></p><p id="p0150" class="elsevierStylePara elsevierViewall">This causality measure is zero if &#936;12e&#8722;i&#969;2&#61;0&#44; which means that the variable <span class="elsevierStyleItalic">CREDIT</span> does not cause <span class="elsevierStyleItalic">GDP</span> at frequency <span class="elsevierStyleItalic">&#969;</span>&#46; The causality of <span class="elsevierStyleItalic">GDP</span> to <span class="elsevierStyleItalic">CREDIT</span> is built using a similar approach&#46;</p></span></span><span id="sec0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">4</span><span class="elsevierStyleSectionTitle" id="sect0065">Data</span><p id="p0155" class="elsevierStylePara elsevierViewall">Our dataset &#40;quarterly frequency&#41; consists of credit to the private non-financial sector and real GDP for 33 economies&#46;<a class="elsevierStyleCrossRef" href="#fn0030"><span class="elsevierStyleSup">6</span></a> Following <a class="elsevierStyleCrossRefs" href="#bib0005">Aikman&#44; Haldane&#44; and Nelson &#40;2011&#41;&#44; Jord&#224;&#44; Schularick&#44; and Taylor &#40;2011&#41;&#44; Schularick and Taylor &#40;2012&#41;</a>&#44; we use the credit cycle as a proxy for the financial cycle&#46; In spite of a general lack of consensus on its exact definition&#44; some studies have shown that the most parsimonious definition of the financial cycle is in terms of credit&#46;<a class="elsevierStyleCrossRef" href="#fn0035"><span class="elsevierStyleSup">7</span></a> Most data on credit&#44; adjusted for breaks&#44; were obtained from the Bank for International Settlements &#40;BIS&#41;&#46; For countries not included in the BIS database &#40;namely Colombia&#44; Chile and Peru&#41; we collected official data from each central bank&#46; All observations on variables were collected for the longest available period&#44; ranging from the early 1940s until the second quarter of 2013 &#40;see <a class="elsevierStyleCrossRef" href="#tbl0005">Appendix 1</a> for a summary of descriptive statistics for each country&#41;&#46; The nominal GDP and the Consumer Price Index &#40;CPI&#41; were both obtained from the International Financial Statistics database of the International Monetary Fund&#46; All data used in this study are expressed in constant prices &#40;using the CPI of each country as deflator&#41;&#46;<a class="elsevierStyleCrossRef" href="#fn0040"><span class="elsevierStyleSup">8</span></a></p><elsevierMultimedia ident="tbl0005"></elsevierMultimedia></span><span id="sec0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">5</span><span class="elsevierStyleSectionTitle" id="sect0070">Empirical results</span><p id="p0160" class="elsevierStylePara elsevierViewall"><a class="elsevierStyleCrossRefs" href="#fig0005">Figs&#46; 1&#8211;3</a> illustrate our main results based on coherence statistics&#44; cross-correlations in the frequency domain and Granger causality tests&#44; respectively&#46; In order to improve readability and to allow a comparison of our results with previous studies&#44; we transformed the frequency domain into time intervals &#40;quarters&#41;&#44; using the following function&#58;</p><p id="p0165" class="elsevierStylePara elsevierViewall"><elsevierMultimedia ident="eq0040"></elsevierMultimedia></p><p id="p0170" class="elsevierStylePara elsevierViewall">Consequently&#44; we followed <a class="elsevierStyleCrossRef" href="#bib0075">Drehmann et al&#46; &#40;2012&#41;</a> to establish conventional duration horizons for short &#40;5&#8211;32 quarters&#41;&#44; medium &#40;32&#8211;80 quarters&#41; and long-term &#40;more than 80 quarters&#41; cycles&#46;</p><p id="p0175" class="elsevierStylePara elsevierViewall"><a class="elsevierStyleCrossRef" href="#fig0005">Fig&#46; 1</a> shows results of computing coherence statistics between credit and GDP for all 33 countries&#46; High values of coherence correspond to a high correlation between credit and GDP&#46; Results show that credit and GDP cycles appear to have greater correlation at medium and long-term frequencies for most countries in our sample &#40;29 out of 33&#41;&#46; This fact highlights the importance of looking at medium and long-term credit cycles when designing macro-prudential policies&#46; The only exceptions include Belgium&#44; India&#44; Mexico and Peru&#44; for which the greater values of coherence occur at short-term frequencies&#46;</p><elsevierMultimedia ident="fig0005"></elsevierMultimedia><p id="p0180" class="elsevierStylePara elsevierViewall"><a class="elsevierStyleCrossRef" href="#fig0010">Fig&#46; 2</a> depicts the cross-correlation between credit and GDP&#46; Each sub-figure comprises the correlation between credit lags and contemporaneous GDP &#40;negative values on the horizontal axis&#41; as well as the correlation between GDP lags and contemporaneous credit &#40;positive values on the horizontal axis&#41; for the whole frequency spectrum&#46; All countries &#40;except Korea&#41; exhibit global maxima at the left hand-side of the domain&#44; indicating a positive relationship between lags in credit and contemporaneous output&#44; specially marked in frequencies corresponding to the medium term&#46; Conversely&#44; all countries present a global minimum at the right hand-side of the domain&#46; This is evidence of a negative correlation between GDP lags and contemporaneous credit&#44; again with a higher influence over the medium term&#46; In sum&#44; credit cycle peaks precede booms in output&#44; while peaks in the output cycle precede troughs in the credit cycle&#46; This result corroborates the findings obtained by <a class="elsevierStyleCrossRef" href="#bib0150">Schularick and Taylor &#40;2012&#41;</a>&#46;</p><elsevierMultimedia ident="fig0010"></elsevierMultimedia><p id="p0185" class="elsevierStylePara elsevierViewall"><a class="elsevierStyleCrossRef" href="#fig0015">Fig&#46; 3</a> depicts results for Granger causality tests between credit and GDP&#44; based on the methodology proposed by <a class="elsevierStyleCrossRef" href="#bib0040">Breitung and Candelon &#40;2006&#41;</a>&#46; We performed this test for the 33 countries in our sample and for the two directions of causality&#46;<a class="elsevierStyleCrossRef" href="#fn0045"><span class="elsevierStyleSup">9</span></a> Therefore&#44; <a class="elsevierStyleCrossRef" href="#fig0015">Fig&#46; 3</a> consists of 66 sub-figures&#46; The horizontal line represents the critical value at the 95&#37; significance level &#40;values above the critical value indicate causality at a given frequency&#41;&#46; Results show that causality runs in both directions &#40;stronger in medium and long-term frequencies&#41;&#44; for most casas&#46; There is however&#44; some heterogeneity among countries&#46; For instance&#44; there are countries like Peru for which causality runs in only one direction&#46; Finally&#44; medium and long-term causality is the most evident&#46;</p><elsevierMultimedia ident="fig0015"></elsevierMultimedia><p id="p0190" class="elsevierStylePara elsevierViewall">Our findings shed light on salient features of macroeconomic modeling&#46; The relationship between financial and real variables is complex&#44; and financial factors influence economic activity beyond exogenous shocks&#46; Overall&#44; we illustrate that GDP and credit cycles are not perfectly synchronized&#46; The relationship between these two cycles is stronger when lags are included&#46; An interesting implication for monetary policy is that it is difficult to target both financial and real variables using just one instrument&#46; Moreover&#44; credit should not be ignored when the objective is to stabilize the economy&#44; as credit cycles exert important influence over the business cycle&#44; and vice-versa&#46;</p><p id="p0195" class="elsevierStylePara elsevierViewall">These results are similar to those found in <a class="elsevierStyleCrossRef" href="#bib0075">Drehmann et al&#46; &#40;2012&#41;</a> in the sense that financial cycles are mostly related to business cycles in the medium-term&#46; In our sample&#44; these results hold true for developed countries such as Australia&#44; Germany&#44; Japan&#44; Norway&#44; Sweden&#44; United Kingdom and the United States&#46; However&#44; while <a class="elsevierStyleCrossRef" href="#bib0075">Drehmann et al&#46; &#40;2012&#41;</a> focus on the time domain&#44; our focus lies in the frequency domain&#46; Our study also focuses on Granger-type causality tests instead of conducting correlation analysis&#46;</p></span><span id="sec0045" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">6</span><span class="elsevierStyleSectionTitle" id="sect0075">Conclusions</span><p id="p0200" class="elsevierStylePara elsevierViewall">We study the relationship between financial and real business cycles for thirty-three countries in the frequency domain&#46; Our sample includes both developed and emerging market economies which allow us to make several benchmark comparisons&#46; We contribute to the literature by first&#44; characterizing the interdependence of credit and output cycles in the frequency domain&#46; In contrast to most of the existing literature&#44; our study avoids issues that center on the differences between short and long-term analysis of cycles&#46; Second&#44; we perform Granger-type causality tests in the frequency domain&#46; By doing so&#44; we obtain stronger results than studies using cross-correlation coefficients&#46;</p><p id="p0205" class="elsevierStylePara elsevierViewall">Our paper confirms some of the previous findings from the literature but also uncovers new results&#46; Similar to <a class="elsevierStyleCrossRef" href="#bib0035">Borio &#40;2011&#41;</a> and <a class="elsevierStyleCrossRef" href="#bib0075">Drehmann et al&#46; &#40;2012&#41;</a>&#44; we find that the likelihood of cycle interdependence is highest when considering medium and long-term frequencies&#46; However&#44; in contrast to most of the related literature&#44; we find that Granger causality runs in both directions&#46;</p><p id="p0210" class="elsevierStylePara elsevierViewall">As such&#44; monetary authorities can benefit by focusing on medium and long-term credit cycles when designing macro-prudential policies&#46; Moreover&#44; credit cycles should be carefully analyzed when trying to stabilize the economy&#44; as they exert important influence over the business cycle&#44; and vice-versa&#46; We believe that our findings elicit key structural differences between emerging and developed economies that can potentially serve as building blocks for the construction of future theoretical models&#46;</p></span><span id="sec0050" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0080">Conflict of interests</span><p id="p0215" class="elsevierStylePara elsevierViewall">The authors declare no conflict of interest&#46;</p></span></span>"
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            2 => "C38"
          ]
        ]
      ]
      "es" => array:2 [
        0 => array:4 [
          "clase" => "keyword"
          "titulo" => "Palabras clave"
          "identificador" => "xpalclavsec814691"
          "palabras" => array:3 [
            0 => "Dominio de la frecuencia"
            1 => "Causalidad de Granger"
            2 => "Interdependencia entre ciclos financieros y reales"
          ]
        ]
        1 => array:4 [
          "clase" => "jel"
          "titulo" => "C&#243;digos JEL"
          "identificador" => "xpalclavsec814692"
          "palabras" => array:3 [
            0 => "E32"
            1 => "E44"
            2 => "C38"
          ]
        ]
      ]
    ]
    "tieneResumen" => true
    "resumen" => array:2 [
      "en" => array:2 [
        "titulo" => "Abstract"
        "resumen" => "<span id="abst0005" class="elsevierStyleSection elsevierViewall"><p id="spar0005" class="elsevierStyleSimplePara elsevierViewall">The history of economic recessions has shown that every deep downturn has been accompanied by disruptions in the financial sector&#46; Paradoxically&#44; up until the financial world crisis of 2007&#8211;2009&#44; little attention was given to macroeconomic and financial interdependence&#46; In this paper&#44; a study is conducted on the relationship between financial and real business cycles for a sample of thirty-three countries in the frequency domain&#46; Specifically&#44; the features of the interdependence of credit and output cycles are analysed and Granger-type causality tests are carried out in the frequency domain&#46; The main findings of the study indicate that the likelihood of cycle interdependence is highest when considering medium and long-term frequencies&#44; and that Granger causality runs in both directions&#46;</p></span>"
      ]
      "es" => array:2 [
        "titulo" => "Resumen"
        "resumen" => "<span id="abst0010" class="elsevierStyleSection elsevierViewall"><p id="spar0010" class="elsevierStyleSimplePara elsevierViewall">Tradicionalmente&#44; las recesiones han venido acompa&#241;adas de disrupciones importantes en el funcionamiento del sistema financiero&#46; Parad&#243;jicamente&#44; antes de la crisis financiera de 2007-2009 se daba muy poca importancia a la interdependencia entre la macroeconom&#237;a y el sistema financiero&#46; En este documento estudiamos la relaci&#243;n entre ciclos financieros y ciclos de producto para una muestra de treinta y tres pa&#237;ses&#46; Espec&#237;ficamente&#44; caracterizamos la interdependencia entre estos ciclos y realizamos pruebas de causalidad tipo Granger en el dominio de la frecuencia&#46; Nuestros resultados principales indican que la mayor interdependencia ocurre en frecuencias asociables al mediano y largo plazo&#46; Encontramos evidencia de causalidad bi-direccional&#46;</p></span>"
      ]
    ]
    "NotaPie" => array:10 [
      0 => array:2 [
        "etiqueta" => "&#9734;"
        "nota" => "<p class="elsevierStyleNotepara" id="npar0005">The findings&#44; recommendations&#44; interpretations and conclusions expressed in this paper are those of the authors and not necessarily reflect the view of the Banco de la Republica nor its Board of Directors&#46;</p>"
      ]
      1 => array:3 [
        "etiqueta" => "1"
        "nota" => "<p class="elsevierStyleNotepara" id="npar0010">Minsky identified three different types of debt structures under which firms can be categorized&#58; &#40;1&#41; hedge finance&#44; &#40;2&#41; speculative finance&#44; and &#40;3&#41; Ponzi finance&#46; The first corresponds to when cash flows are sufficient for paying interest and principal debt payments&#46; The second corresponds to paying interest payments &#40;but not for repaying the principal&#41;&#46; Thus&#44; firms under this category need to issue new debt&#46; Finally&#44; the third correspond to when cash flows are not enough to even cover interest payments&#46; Firms under this last category are in continuous need to refinance their debts&#44; and are extremely vulnerable to changes in current and future short-term interest rates&#46;</p>"
        "identificador" => "fn0005"
      ]
      2 => array:3 [
        "etiqueta" => "2"
        "nota" => "<p class="elsevierStyleNotepara" id="npar0015">Minsky argues that&#44; under monetary tightening&#44; speculative firms will turn into Ponzi&#44; and the net worth of firms that were already in Ponzi positions will decrease and will have to compensate for cash flow shortfalls by selling liquid assets&#46; If fire-sales are sufficiently large&#44; market values may collapse&#44; increasing the likelihood of a debt-deflationary process&#46;</p>"
        "identificador" => "fn0010"
      ]
      3 => array:3 [
        "etiqueta" => "3"
        "nota" => "<p class="elsevierStyleNotepara" id="npar0020">Granger-type causality tests using filtered data can potentially corrupt empirical estimates &#40;see <a class="elsevierStyleCrossRefs" href="#bib0020">Barnett &#38; Seth&#44; 2011&#59; Florin&#44; Gross&#44; Pfeifer&#44; Fink&#44; &#38; Timmermann&#44; 2010&#59; Geweke&#44; 1982</a>&#41;&#46; In essence these studies show that filtering is inappropriate for isolating causal influences within specific frequency bands&#46; However&#44; we believe that this limitation falls outside the scope of our study and should be analyzed in future research&#44; given its importance&#46;</p>"
        "identificador" => "fn0015"
      ]
      4 => array:3 [
        "etiqueta" => "4"
        "nota" => "<p class="elsevierStyleNotepara" id="npar0025">See <a class="elsevierStyleCrossRef" href="#bib0145">Pollock &#40;2000&#41;</a>&#46;</p>"
        "identificador" => "fn0020"
      ]
      5 => array:3 [
        "etiqueta" => "5"
        "nota" => "<p class="elsevierStyleNotepara" id="npar0030">Variables in <span class="elsevierStyleItalic">Z</span><span class="elsevierStyleInf"><span class="elsevierStyleItalic">t</span></span> correspond to the stationary cyclical component of the series&#46; Hence&#44; cointegration analysis does not apply&#46;</p>"
        "identificador" => "fn0025"
      ]
      6 => array:3 [
        "etiqueta" => "6"
        "nota" => "<p class="elsevierStyleNotepara" id="npar0035">Australia&#44; Austria&#44; Belgium&#44; Brazil&#44; Canada&#44; Chile&#44; Colombia&#44; Czech Republic&#44; Denmark&#44; Finland&#44; France&#44; Germany&#44; Hungary&#44; India&#44; Indonesia&#44; Ireland&#44; Italy&#44; Japan&#44; South Korea&#44; Mexico&#44; New Zealand&#44; Norway&#44; Peru&#44; Poland&#44; Portugal&#44; Russia&#44; South Africa&#44; Spain&#44; Sweden&#44; Switzerland&#44; Turkey&#44; United Kingdom and United States&#46;</p>"
        "identificador" => "fn0030"
      ]
      7 => array:3 [
        "etiqueta" => "7"
        "nota" => "<p class="elsevierStyleNotepara" id="npar0040">See&#44; for instance&#44; <a class="elsevierStyleCrossRef" href="#bib0035">Borio &#40;2011&#41;</a>&#46;</p>"
        "identificador" => "fn0035"
      ]
      8 => array:3 [
        "etiqueta" => "8"
        "nota" => "<p class="elsevierStyleNotepara" id="npar0045">For comparability purposes we use the CPI instead of the GDP deflator to express variables in real terms&#46; Studies such as <a class="elsevierStyleCrossRef" href="#bib0075">Drehmann et al&#46; &#40;2012&#41;</a> and <a class="elsevierStyleCrossRef" href="#bib0105">Gomez-Gonzalez&#44; Ojeda-Joya&#44; Z&#225;rate&#44; &#38; Tenjo-Galarza &#40;2014&#41;</a> also use the CPI as the price deflator&#46;</p>"
        "identificador" => "fn0040"
      ]
      9 => array:3 [
        "etiqueta" => "9"
        "nota" => "<p class="elsevierStyleNotepara" id="npar0050">All data were de-trended before performing these tests&#46;</p>"
        "identificador" => "fn0045"
      ]
    ]
    "multimedia" => array:12 [
      0 => array:7 [
        "identificador" => "fig0005"
        "etiqueta" => "Fig&#46; 1"
        "tipo" => "MULTIMEDIAFIGURA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "figura" => array:3 [
          0 => array:1 [
            "imagen" => "gr1a.jpeg"
          ]
          1 => array:1 [
            "imagen" => "gr1b.jpeg"
          ]
          2 => array:1 [
            "imagen" => "gr1c.jpeg"
          ]
        ]
        "descripcion" => array:1 [
          "en" => "<p id="spar0015" class="elsevierStyleSimplePara elsevierViewall">Coherence Statistics between GDP and Credit by Country &#40;Frequency Domain &#8211; Horizontal axis measured in quarters&#41;&#46;</p>"
        ]
      ]
      1 => array:7 [
        "identificador" => "fig0010"
        "etiqueta" => "Fig&#46; 2"
        "tipo" => "MULTIMEDIAFIGURA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "figura" => array:3 [
          0 => array:1 [
            "imagen" => "gr2a.jpeg"
          ]
          1 => array:1 [
            "imagen" => "gr2b.jpeg"
          ]
          2 => array:1 [
            "imagen" => "gr2c.jpeg"
          ]
        ]
        "descripcion" => array:1 [
          "en" => "<p id="spar0020" class="elsevierStyleSimplePara elsevierViewall">Cross-correlations in the frequency domain between GDP and Credit by Country &#40;Angular frequency in horizontal axis- vs&#46; Correlation Coefficient in vertical axis&#41;&#46; The two points marked in each graph are the maximum correlations from lagged GDP to contemporaneous Credit and vice-versa&#46;</p>"
        ]
      ]
      2 => array:7 [
        "identificador" => "fig0015"
        "etiqueta" => "Fig&#46; 3"
        "tipo" => "MULTIMEDIAFIGURA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "figura" => array:4 [
          0 => array:1 [
            "imagen" => "gr3a.jpeg"
          ]
          1 => array:1 [
            "imagen" => "gr3b.jpeg"
          ]
          2 => array:1 [
            "imagen" => "gr3c.jpeg"
          ]
          3 => array:1 [
            "imagen" => "gr3d.jpeg"
          ]
        ]
        "descripcion" => array:1 [
          "en" => "<p id="spar0025" class="elsevierStyleSimplePara elsevierViewall">Causality Tests by Country in the Frequency Domain &#40;Quarters in the horizontal axis vs&#46; Statistic of Granger&#39;s Causality Test in the vertical axis&#41;&#46; The horizontal line in each of the graphs represents the critical values for the Granger&#39;s causality test&#46; Angular frequency values above it suggest that variables in these frequencies cause one another&#46;</p>"
        ]
      ]
      3 => array:7 [
        "identificador" => "tbl0005"
        "etiqueta" => "Appendix 1"
        "tipo" => "MULTIMEDIATABLA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "tabla" => array:2 [
          "leyenda" => "<p id="spar0035" class="elsevierStyleSimplePara elsevierViewall">Source&#58; Authors&#8217; calculations&#46; P25 and P75 correspond to the bottom 25&#37; and top 75&#37; values&#44; respectively&#46;</p>"
          "tablatextoimagen" => array:1 [
            0 => array:2 [
              "tabla" => array:1 [
                0 => """
                  <table border="0" frame="\n
                  \t\t\t\t\tvoid\n
                  \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="table-head  " align="left" valign="top" scope="col">Country&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</th><th class="td" title="table-head  " align="left" valign="top" scope="col">Time Period&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</th><th class="td" title="table-head  " colspan="3" align="left" valign="top" scope="col" style="border-bottom: 2px solid black">GDP</th><th class="td" title="table-head  " colspan="3" align="left" valign="top" scope="col" style="border-bottom: 2px solid black">CREDIT</th></tr><tr title="table-row"><th class="td" title="table-head  " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</th><th class="td" title="table-head  " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</th><th class="td" title="table-head  " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">P25&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</th><th class="td" title="table-head  " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">MEDIAN&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</th><th class="td" title="table-head  " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">P75&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</th><th class="td" title="table-head  " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">P25&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</th><th class="td" title="table-head  " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">MEDIAN&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</th><th class="td" title="table-head  " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">P75&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Australia&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">Jun-59&#8211;Jun-13&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">2&#46;31&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">3&#46;58&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">4&#46;98&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">3&#46;57&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">6&#46;44&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">9&#46;32&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Austria&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">Jun-59&#8211;Jun-13&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">1&#46;42&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">2&#46;83&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">4&#46;39&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">3&#46;33&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">6&#46;53&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">10&#46;89&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Belgium&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">Jan-73&#8211;Jun-13&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;85&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">2&#46;07&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">2&#46;96&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">2&#46;96&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">5&#46;78&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">8&#46;91&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Brazil&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">Jan-96&#8211;Jun-13&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;86&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">2&#46;77&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">4&#46;90&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;59&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">9&#46;32&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">16&#46;87&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Canada&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">Jun-59&#8211;Jun-13&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">1&#46;99&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">3&#46;61&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">5&#46;09&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">2&#46;69&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">5&#46;25&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">8&#46;80&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Chile&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">Jun-82&#8211;Jun-13&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">3&#46;44&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">5&#46;53&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">7&#46;26&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">&#8722;3&#46;41&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">1&#46;40&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">5&#46;82&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Colombia&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">Jun-79&#8211;Jun-13&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">2&#46;34&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">3&#46;87&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">5&#46;44&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">&#8722;15&#46;99&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">&#8722;9&#46;46&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">&#8722;4&#46;86&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Czech Republic&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">Jun-95&#8211;Jun-13&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;38&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">3&#46;19&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">5&#46;23&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">&#8722;1&#46;48&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">3&#46;69&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">9&#46;02&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Denmark&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">Jun-59&#8211;Jun-13&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;01&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">1&#46;75&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">3&#46;08&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">1&#46;75&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">5&#46;26&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">9&#46;11&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Finland&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">Jan-73&#8211;Jun-13&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">1&#46;29&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">3&#46;07&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">4&#46;92&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">2&#46;29&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">5&#46;54&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">8&#46;13&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">France&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">Jan-72&#8211;Jun-13&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">1&#46;11&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">2&#46;07&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">3&#46;30&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">1&#46;55&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">3&#46;58&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">6&#46;83&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Germany&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">Jun-59&#8211;Jun-13&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;87&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">2&#46;58&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">4&#46;18&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">2&#46;16&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">3&#46;99&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">7&#46;12&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Hungary&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">Jan-92&#8211;Jun-13&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;35&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">3&#46;04&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">4&#46;16&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">&#8722;5&#46;28&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">8&#46;53&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">15&#46;41&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Iceland&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">Jun-06&#8211;Jun-13&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;44&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">2&#46;53&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">6&#46;24&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">&#8722;17&#46;09&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">29&#46;12&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">39&#46;03&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">India&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">Jun-59&#8211;Jun-13&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">5&#46;26&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">6&#46;66&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">8&#46;95&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">3&#46;20&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">6&#46;91&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">11&#46;87&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Indonesia&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">Sep-78&#8211;Jun-13&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">4&#46;26&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">5&#46;42&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">6&#46;14&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">3&#46;07&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">11&#46;92&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">19&#46;10&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Ireland&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">Sep-73&#8211;Jun-13&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;35&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">4&#46;61&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">6&#46;85&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">2&#46;35&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">7&#46;79&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">13&#46;84&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Italy&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">Jun-59&#8211;Jun-13&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;39&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">1&#46;60&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">2&#46;76&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">1&#46;13&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">6&#46;61&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">9&#46;62&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Japan&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">Jan-67&#8211;Jun-13&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">1&#46;38&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">4&#46;07&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">7&#46;67&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">&#8722;0&#46;83&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">3&#46;65&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">9&#46;20&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Korea&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">Jun-72&#8211;Jun-13&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">4&#46;32&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">6&#46;93&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">10&#46;30&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">6&#46;36&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">11&#46;98&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">16&#46;35&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Luxembourg&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">Jun-05&#8211;Jun-13&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">1&#46;55&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">3&#46;17&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">6&#46;40&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">1&#46;52&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">9&#46;04&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">29&#46;93&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Mexico&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">Jan-83&#8211;Jun-13&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">1&#46;06&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">3&#46;17&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">4&#46;71&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">&#8722;9&#46;09&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">4&#46;67&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">16&#46;77&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">New Zealand&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">Jun-90&#8211;Jun-13&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">1&#46;14&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">2&#46;97&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">4&#46;33&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">2&#46;87&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">6&#46;60&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">9&#46;35&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Norway&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">Jan-56&#8211;Jun-13&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">1&#46;24&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">3&#46;04&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">4&#46;92&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">2&#46;34&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">5&#46;01&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">8&#46;60&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Peru&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">Jun-96&#8211;Jun-13&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">1&#46;78&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">5&#46;40&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">7&#46;51&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">&#8722;5&#46;07&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">5&#46;82&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">16&#46;42&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Poland&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">Jun-94&#8211;Jun-13&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">2&#46;40&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">4&#46;33&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">5&#46;84&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">1&#46;18&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">11&#46;16&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">22&#46;67&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Portugal&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">Jun-59&#8211;Jun-13&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;72&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">2&#46;27&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">4&#46;10&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">1&#46;43&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">6&#46;91&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">11&#46;02&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Russia&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">Sep-97&#8211;Jun-13&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">1&#46;50&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">4&#46;86&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">8&#46;03&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">2&#46;69&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">17&#46;58&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">25&#46;73&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Slovenia&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">Jun-09&#8211;Jun-13&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">2&#46;05&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">3&#46;79&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">5&#46;33&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;04&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">4&#46;55&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">16&#46;43&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">South Africa&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">Jun-67&#8211;Jun-13&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">1&#46;63&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">3&#46;46&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">5&#46;26&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;65&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">5&#46;15&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">9&#46;22&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Spain&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">Jun-72&#8211;Jun-13&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">1&#46;05&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">2&#46;79&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">4&#46;07&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;88&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">5&#46;85&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">12&#46;26&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Sweden&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">Jun-63&#8211;Jun-13&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;71&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">2&#46;69&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">3&#46;92&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">1&#46;32&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">3&#46;99&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">9&#46;25&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Switzerland&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">Sep-77&#8211;Jun-13&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;48&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">2&#46;28&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">3&#46;46&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;81&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">3&#46;17&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">6&#46;14&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Turkey&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">Jun-88&#8211;Jun-13&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">1&#46;38&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">5&#46;41&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">8&#46;07&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">&#8722;3&#46;63&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">8&#46;94&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">19&#46;87&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">United Kingdom&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">Jun-90&#8211;Jun-13&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">1&#46;51&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">2&#46;51&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">3&#46;89&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">1&#46;25&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">5&#46;98&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">9&#46;05&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">United States&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">Jun-59&#8211;Jun-13&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">1&#46;85&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">3&#46;16&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">4&#46;82&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">2&#46;41&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">5&#46;00&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">7&#46;09&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr></tbody></table>
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Article information

ISSN: 01204483
Original language: English

DOI:10.1016/j.espe.2015.05.002

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Year/Month	Html	Pdf	Total

2024 October	16	1	17
2024 September	26	6	32
2024 August	29	2	31
2024 July	18	11	29
2024 June	14	4	18
2024 May	18	12	30
2024 April	31	7	38
2024 March	31	13	44
2024 February	28	7	35
2024 January	17	0	17
2023 December	30	15	45
2023 November	30	3	33
2023 October	35	12	47
2023 September	26	2	28
2023 August	17	9	26
2023 July	14	3	17
2023 June	11	3	14
2023 May	22	13	35
2023 April	30	6	36
2023 March	25	3	28
2023 February	22	9	31
2023 January	35	60	95
2022 December	29	9	38
2022 November	42	11	53
2022 October	49	27	76
2022 September	25	17	42
2022 August	32	17	49
2022 July	26	11	37
2022 June	20	6	26
2022 May	33	13	46
2022 April	24	15	39
2022 March	61	16	77
2022 February	80	17	97
2022 January	52	8	60
2021 December	57	18	75
2021 November	54	19	73
2021 October	67	25	92
2021 September	47	12	59
2021 August	25	5	30
2021 July	32	13	45
2021 June	46	13	59
2021 May	23	15	38
2021 April	126	19	145
2021 March	59	18	77
2021 February	30	8	38
2021 January	29	11	40
2020 December	26	8	34
2020 November	40	10	50
2020 October	20	4	24
2020 September	25	15	40
2020 August	42	18	60
2020 July	31	17	48
2020 June	20	10	30
2020 May	30	12	42
2020 April	25	5	30
2020 March	32	4	36
2020 February	30	8	38
2020 January	46	7	53
2019 December	29	5	34
2019 November	25	6	31
2019 October	39	7	46
2019 September	44	6	50
2019 August	38	7	45
2019 July	41	9	50
2019 June	57	39	96
2019 May	100	60	160
2019 April	63	39	102
2019 March	13	8	21
2019 February	33	9	42
2019 January	18	5	23
2018 December	25	8	33
2018 November	19	6	25
2018 October	40	16	56
2018 September	23	5	28
2018 August	55	13	68
2018 July	45	8	53
2018 June	28	0	28
2018 May	32	2	34
2018 April	26	2	28
2018 March	21	0	21
2018 February	17	0	17
2018 January	83	1	84
2017 December	20	3	23
2017 November	58	0	58
2017 October	21	1	22
2017 September	23	2	25
2017 August	25	2	27
2017 July	48	5	53
2017 June	37	2	39
2017 May	51	5	56
2017 April	27	8	35
2017 March	42	3	45
2017 February	39	3	42
2017 January	31	2	33
2016 December	46	10	56
2016 November	46	3	49
2016 October	38	4	42
2016 September	41	4	45
2016 August	34	8	42
2016 July	20	4	24
2016 June	39	14	53
2016 May	32	16	48
2016 April	27	21	48
2016 March	64	18	82
2016 February	42	33	75
2016 January	52	36	88
2015 December	37	40	77

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