Business Cycle Synchronization in Latin America

Business Cycle Synchronization in Latin AmericaBusiness trolls synchronization in Latin AmericaA TVTPMS ApproachIntroductionOver the last decades, there has been a growing disport in the occupation cycle transmissions among countries and interdependencies. The design of regional co-operations and integrations, such as Mercosur or Latin America countries, has the purpose to reduce poverty, amplify society welf are and enhance macroeconomic stability. However, it is crucial to understand the influence of regional integration and the role of external actor ins on regional craft cycle synchronization.Fiess (..) find that a relatively low degree of melodic phrase cycle synchronization deep down Central America as well as between Central America and the United States. Grigoli (2009) analyzed the causation relations among business activities of the Mercosur countries to determine which cycles are dependent on others, considering trade intensity, trade structure and the influences o f the EU and US as well. He find some causation relations among the South-American countries however, the EU and US do not play a relevant role in determining the fluctuations of their cycles. Gutierrez and Gomes (..) use the Beveridge-Nelson-Stock-Watson multivariate trend-cycle decomposition model to estimate a general trend and jet cycle. Aiolfi et al. (2010) identify a sizeable common component in the LA countries business cycles, suggesting the existence of a regional cycle Caporale and Girardi (2012) yield that the LA region as a whole is largely dependent on external developments and the trade channel appears to be the most important artificial lake of business cycle co-movement. They report that the business cycle of the individual LA countries appears to be influenced by country-specific, regional and external shocks in a very nonuniform way.In order to investigate the degree of synchronization of the business cycles among the six major LA economies1 (namely, Argentina , brazil nut, chili con carne, Mexico, Colombia and Venezuela) as a whole, we consider the presence of a regional cycle by estimating the common out suppuration cycle with the aim of testing its effect on each country-specific cycle.Besides this introduction, this paper is organized as follows. partitioning 2 contains the model and describes the data. Section 3 presents the empirical results and finally, section 4 concludes.Data and Methodology We use quarterly data of the real gross domestic product increment rate of the LAC countries, extracted from Penn World Table , namely , covering the period from the first quarter of to the last quarter of .We focus on whether the economic activity in the LAC countries is driven by a joint business cycle. We first look at the engine of growth lies within the LA countries. We therefore firstly jump by studying the existence of a common cycle among the economies studied. Second, we attempt to find the influence of a common factor in refe rred to as the LACs business cycle extracted from the estimation of a dynamic common factor model.We employ a measure of business cycles synchronization based on Hamiltons (1989) true Markov-switching model and the magazine varying Markovswitching model developed by Filardo (1994) and reconsidered recently by Kim et al. (2008) to investigate the regional common factor in date the regional business cycles. This study analyzes whether the synchronization pattern of business cycles in a country has systematically changed with the magnification or respite phases of regional business cycle. In this context, we fancied business cycles in a particular country are driven by regional cycles proxied by the common dynamic factor in real GDP growth of the LA countries, thus we use a dynamic factor model to extract the regional cycle. The main interest of the analysis is that a latent dynamic factor drives the co-movement of a high-dimensional vector of time-series variables which is also a ffected by a vector of mean-zero idiosyncratic disturbances, t (Stock, 2010). The common factors are assumed to follow a first-order autoregressive process. This linear responsibility-space model can be written as follows (1) (2)where L1,t,,Lk,t are common to all the series, and are freelance Gaussian white noise terms. The L matrix of factor loadings measures the instantaneous advert of the common factors on each series.There are two growth phases or government activitys with a transition between them governed by a time-varying transition opportunity matrix. The advantage of such a model is that the administrations can be substantially interpreted as regimes of recession and elaboration. The estimated equation is the following2 , (3)where and The endogenous variable, yt (the real growth rate in a given country at time t) is assumed to visit the two states of a hidden variable, st, that follows a first-order Markov chain, over the T observations3. st, , are real coefficie nts to be estimated. Denoting zt the starring(p) variable (the regional common factor at time t), we want to know whether zt causes yt+k, k= 1,2, .Under the assumption that both y and z have ergodic distributions, we define the following transition probability functions (4)where and are elements of the following transition probability matrix (5)with Pij the probability of switching from regime j at time t 1 to regime i at time t and i, j =1, 2 with for all i,j1,2. k is a lag. In order to estimate the coefficients of equation (1), we need to maximize the log-likelihood of the categoric density function of yt (6)The unconditional density function is the product of the conditional density function and the unconditional probability of st. This is written as4 (7)Transition probabilities indicate that the states of expansion and recession are equally persistent, and this persistency is very strong. These probabilities aim to provide information about the likelihood of staying or swit ching from a given regime of k periods after a regime change in z. If the estimate of 1 is positive and 2 is negative, then regime 1 can be interpreted as one of expansion and regime 2 as one of contraction. Furthermore, assume that in eq. () 1,2 is positive. This indicates that while any increase in leading indicator (z) increases P11, probability that y stays in regime 1, any decrease in z increases 1-P11, probability that y switches from regime 1 k periods later that is, an expansion (recession) in z leads to an expansion (recession) in another country. Similarly, a negative 1,2 means that an expansion in z leads to a recession in another country. Additionally, a negative 2,2 means that any decrease (increase) in leading indicator increases the probability of staying in regime 2 (switching from regime 2). If both 1,2 and 2,2 are insignificant, this would mean that there is no statistically meaningful impact of the occurrence of expansions or recessions in a leading market on the growth regime of the other markets5.Empirical ResultsFig. 1 refers to the common factor, i.e. the regional growth cycle of the Latin America countries. As we can see, the common factor easily captures the well-known common features of the LA business cycle such as the 199495 Mexican crisis and the Tequila crisis. To test the hypothesis of a joint business cycle in the LA, we estimate the TVTPMS model given by Eqs. (1) and (2) with the variable z referring to the common factor (regional cycle).Fig. 1. Common factor in real GDP growth of the Latin America countriesThe estimation results for the regional cycle as leading variable are reported in Table 1. We find importantly positive 1 and negative 2 which correspond to a situation of distinct expansion and contraction regimes. Our main findings are based on the import of the estimated coefficients 1,2 and 2,2. When looking at the significance of the coefficient 1,2 , it is found that the common factor exerts direct effects on Mexico and Venezuela, implying that a high growth rate in regional cycle is informative of GDP expansion phases in these countries. That is, an expansion in common factor increases the probability that Mexico and Venezuela will continue to evolve in an expansion regime (i.e. P11). However, we see that 2,2 is never significant for these countries. This suggests that the regional cycle can never be considered as a leading indicator of the future state of the cycle in Mexico and Venezuela when they are already in the contraction regime (i.e. P22 and P1-22). Conversely, our results show that regional cycle is sensitive to economic fluctuations in Colombia, Chile and Brazil because 2,2 is significant, thereby implying that any change in regional factor does help predict whether these economies will stay into or escape from contractions.Table 1 musical theme results for the regional cycle as leading variable. The numbers in bold indicate that a high growth rate in Mexico, Venezuela, Colombia, Ch ile, and the Brail has an impact on the expansion and recession phases of the regional cycle.The evidence presented here indicates that Latin America countries increasing economic interdependence has strengthened both interregional business cycles synchronization. A regional cycle could provide significant informational content in predicting the future state of Mexico and Venezuela only when they are already into the expansionary state and the future state of Colombia, Chile and Mexico when they are already in the contraction regime. That is, the high level of integration reached within the region has enabled Mexico and Venezuela to emerge as a pole of economic growth where their business cycles are mutually reinforced during expansions. In other words, while this increasing economic interdependence tends to strengthen output co-movements when these countries are already in the expansionary state, the shift from contractions to recovery, opposed to Colombia, Chile and Mexico, do not depend on the recovery in other countries. For Argentina, both 1,2 and 2,2 is insignificant, implying any change in the regional cycle regional cycle is not sensitive to economic fluctuations in this country.ConclusionThe papers other main finding is that a regional cycle could provide significant informational content in predicting the future state of the five of the largest Latin American economiesArgentina, Brazil, Venezuela, Chile, and Mexico. However, the amplitude and duration of the business cycle are asymmetric, indicating that nonlinearities are important in the growth process.Thus, since the Latin America countries business cycles are well-tied together through a regional cycle, the costs of joining a pecuniary union would be reduced if a deeper regional economic cooperation, including intra-exchange rate stability and macroeconomic policy coordination, before turning on to a mature monetary union. Since the Latin American economies have historically been highly dependen t globalization process and demand from outside trading partners it would be interesting repeating a similar exercise with interest rates and cyclical output in advanced countries.ReferencesHamilton, J.D., 1989. A new approach to the economic analysis of nonstationary timeseries and the business cycle. Econometrica 57, 357384.Filardo, A.J., 1994. Business cycle phases and their transitional dynamics. J. Bus. Econ. Stat.12, 299308.1 These countries have accounted for some 70 percent of the regions GDP over the past half century (Maddison, 2003, pp. 134140)2 The lag structure has been tested with standard AIC, HQ and SC criteria.3 The occurrence of a regime is referred by a variable st that takes two determine 1 if the observed regime is 1 and 2 if it is regime 24 The lags in the model are chosen using the Akaike information criterion. Moreover, we perform the Ljung disaster (LB) test to check that there is no residual autocorrelation5 In this case, The TVPMS model converges to the Hamilton fixed probability model