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

Laura Jackson Young, Ayhan Kose, Christopher Otrok and Michael Owyang

A chapter in Dynamic Factor Models, 2016, vol. 35, pp 361-400 from Emerald Group Publishing Limited

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 ofOtrok and Whiteman (1998), the Bayesian state-space approach ofKim 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 fromCesa-Bianchi, Cespedes, and Rebucci (2015)and interpret the empirical results in light of the Monte Carlo results.

Keywords: Principal components; Kalman filter; data augmentation; business cycles; C3; C18; C32; E32 (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (21)

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Working Paper: Specification and Estimation of Bayesian Dynamic Factor Models: A Monte Carlo Analysis with an Application to Global House Price Comovement (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:eme:aecozz:s0731-905320150000035009

DOI: 10.1108/S0731-905320150000035009

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