The empirical literature on common international business cycles has largely ignored model misspecification in estimated factor models as the various cycles are typically imposed but not tested for. This paper proposes a Bayesian stochastic factor selection approach for multilevel factor models. The procedure is applied to a three-level dynamic factor model with a global factor, six regional factors and three development level factors. We estimate the factor model using real GDP growth data for a panel of 60 countries over the period 1961−2017. We find robust evidence for the presence of a global business cycle, four regional cycles (Europe, North America, Latin America and Asia) and two development level cycles (industrial countries and emerging market economies). This suggests that both geographical proximity and the development level of countries are important dimensions of international business cycle synchronization that should be considered simultaneously, a point not previously made in the existing synchronization literature.
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