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An endogenously clustered factor approach to international business cycles

  • Neville Francis
  • Michael T. Owyang
  • Özge Savascin

Factor models have become useful tools for studying international business cycles. Block factor models [e.g., Kose, Otrok, and Whiteman (2003)] can be especially useful as the zero restrictions on the loadings of some factors may provide some economic interpretation of the factors. These models, however, require the econometrician to predefine the blocks, leading to potential misspecification. In Monte Carlo experiments, we show that even small misspecifica- tion can lead to substantial declines in t. We propose an alternative model in which the blocks are chosen endogenously. The model is estimated in a Bayesian framework using a hierarchi- cal prior, which allows us to incorporate series-level covariates that may influence and explain how the series are grouped. Using similar international business cycle data as Kose, Otrok, and Whiteman, we find our country clusters differ in important ways from those identified by geography alone. In particular, we find that similarities in institutions (e.g., legal systems, language diversity) may be just as important as physical proximity for analyzing business cycle comovements.

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Paper provided by Federal Reserve Bank of St. Louis in its series Working Papers with number 2012-014.

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Date of creation: 2012
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Handle: RePEc:fip:fedlwp:2012-014
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  1. Jean Boivin & Serena Ng, 2003. "Are More Data Always Better for Factor Analysis?," NBER Working Papers 9829, National Bureau of Economic Research, Inc.
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  9. Chib S. & Jeliazkov I., 2001. "Marginal Likelihood From the Metropolis-Hastings Output," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 270-281, March.
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  13. Baxter, Marianne & Kouparitsas, Michael A., 2005. "Determinants of business cycle comovement: a robust analysis," Journal of Monetary Economics, Elsevier, vol. 52(1), pages 113-157, January.
  14. Frühwirth-Schnatter, Sylvia & Kaufmann, Sylvia, 2004. "Model-based Clustering of Multiple Time Series," CEPR Discussion Papers 4650, C.E.P.R. Discussion Papers.
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