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Specification and Estimation of Bayesian Dynamic Factor Models: A Monte Carlo Analysis with an Application to Global House Price Comovement

Laura E. Jackson, Ayhan Kose, Christopher Otrok () and Michael Owyang ()
Authors registered in the RePEc Author Service: Laura Jackson Young () and Laura Jackson

No 2015-31, Working Papers from Federal Reserve Bank of St. Louis

Abstract: We compare methods to measure comovement in business cycle data using multi-level dynamic factor models. To do so, we employ a Monte Carlo procedure to evaluate model performance for different specifications of factor models across three different estimation procedures. We consider three general factor model specifications used in applied work. The first is a single- factor model, the second a two-level factor model, and the third a three-level factor model. Our estimation procedures are the Bayesian approach of Otrok and Whiteman (1998), the Bayesian state space approach of Kim and Nelson (1998) and a frequentist principal components approach. The latter serves as a benchmark to measure any potential gains from the more computationally intensive Bayesian procedures. We then apply the three methods to a novel new dataset on house prices in advanced and emerging markets from Cesa-Bianchi, Cespedes, and Rebucci (2015) and interpret the empirical results in light of the Monte Carlo results.

Keywords: Kalman filter; business cycles; data augmentation; principal components (search for similar items in EconPapers)
JEL-codes: C3 (search for similar items in EconPapers)
Pages: 46 pages
Date: 2015-08-26, Revised 2015-08-26
New Economics Papers: this item is included in nep-ecm and nep-ets
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Chapter: Specification and Estimation of Bayesian Dynamic Factor Models: A Monte Carlo Analysis with an Application to Global House Price Comovement (2016) Downloads
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