Empirical Bayes Methods for Dynamic Factor Models

Siem Jan Koopman and Geert Mesters

No 14-061/III, Tinbergen Institute Discussion Papers from Tinbergen Institute

Abstract: We consider the dynamic factor model where the loading matrix, the dynamic factors and the disturbances are treated as latent stochastic processes. We present empirical Bayes methods that enable the efficient shrinkage-based estimation of the loadings and the factors. We show that our estimates have lower quadratic loss compared to the standard maximum likelihood estimates. We investigate the methods in a Monte Carlo study where we document the finite sample properties. Finally, we present and discuss the results of an empirical study concerning the forecasting of U.S. macroeconomic time series using our empirical Bayes methods.

Keywords: Importance sampling; Kalman filtering; Likelihood-based analysis; Posterior modes; Rao-Blackwellization; Shrinkage (search for similar items in EconPapers)
JEL-codes: C32 C43 (search for similar items in EconPapers)
Date: 2014-05-23
New Economics Papers: this item is included in nep-ecm and nep-ets
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Related works:
Journal Article: Empirical Bayes Methods for Dynamic Factor Models (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:tin:wpaper:20140061

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