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This paper studies the changes in world business cycles during the period 1960-2001. We employ a Bayesian dynamic latent factor model to estimate common components in the main macroeconomic aggregates (output, consumption, and investment) of the G7 countries. Using the model, we estimate common and country-specific factors. These factors are used to quantify the relative importance of the common and country components in explaining comovement in each observable aggregate over three distinct time periods: the Bretton Woods (BW) period (1960:1-1972:2), the period of common shocks (1972:3-1986:2), and the globalization period (1986:3-2001:4). We also study how different types of shocks have affected the nature of business cycle comovement over these three periods. We find that the common factor explains a larger fraction of output, consumption and investment volatility in the globalization period than it does in the BW period. The common factor also accounts for a larger fraction of investment variation in the period of globalization than it does in the common shock period. Movements in interest rates seem to be the predominant source of comovement for most countries, with oil prices playing a critical role in Japan and to a lesser extent in the U.K. during the common shock period. The main driver of observed comovement in the globalization period remains unidentified, leaving open the possibility that it involves productivity shocks.ϒ We would like to thank Linda Tesar, Prakash Loungani and Pre-Conference participants for useful suggestions.

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The paper investigates the common dynamic properties of business-cycle fluctuations across countries, regions, and the world. We employ a Bayesian dynamic latent factor model to estimate common components in macroeconomic aggregates (output, consumption, and investment) in a 60-country sample covering seven regions of the world. The results indicate that a common world factor is an important source of volatility for aggregates in most countries, providing evidence for a world business cycle. We find that region-specific factors play only a minor role in explaining fluctuations in economic activity. We also document similarities and differences across regions, countries, and aggregates. (JEL F41, E32, C11, C32)

This paper designs and implements a Bayesian dynamic latent factor model for a vector of data describing the Iowa economy. Posterior distributions of parameters and the latent factor are analyzed by Markov Chain Monte Carlo methods, and coincident and leading indicators are given by posterior mean values of current and predictive distributions for the latent factor.

Abstract:The paper describes a relative entropy procedure for imposing moment restrictions on simulated forecast distributions from a variety of models. Starting from an empirical forecast distribution for some variables of interest, the technique generates a new empirical distribution that satisfies a set of moment restrictions. The new distribution is chosen to be as close as possible to the original in the sense of minimizing the associated Kullback-Leibler Information Criterion, or relative entropy. The authors illustrate the technique by using several examples that show how restrictions from other forecasts and from economic theory may be introduced into a model's forecasts.JEL classification: E44, C53

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