Empirical Bayes Methods for Dynamic Factor Models

• 成果类型: Article
• 署名作者: Koopman, S. J.; Mesters, G.
• 署名单位: Tinbergen Institute; Vrije Universiteit Amsterdam; CREATES; Aarhus University; Pompeu Fabra University; Barcelona School of Economics; Vrije Universiteit Amsterdam
• 刊物名称: REVIEW OF ECONOMICS AND STATISTICS
• ISSN/ISSBN: 0034-6535
• DOI: 10.1162/REST_a_00614
• 发表日期: 2017-07
• 页码: 486-498
• 关键词: maximum-likelihood-estimation; number

摘要

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 shrinkagebased estimation of the loadings and factors. We investigate the methods in a large Monte Carlo study where we evaluate the finite sample properties of the empirical Bayes methods for quadratic loss functions. 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.