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Empirical Bayes Methods for Dynamic Factor Models

Author

Listed:
  • S. J. Koopman

    (VU University Amsterdam, Tinbergen Institute, and CREATES, Aarhus University)

  • G. Mesters

    (Universitat Pompeu Fabra, Barcelona GSE and Netherlands Institute for the Study of Crime and Law Enforcement)

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 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.

Suggested Citation

  • S. J. Koopman & G. Mesters, 2017. "Empirical Bayes Methods for Dynamic Factor Models," The Review of Economics and Statistics, MIT Press, vol. 99(3), pages 486-498, July.
  • Handle: RePEc:tpr:restat:v:99:y:2017:i:3:p:486-498
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    References listed on IDEAS

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    Cited by:

    1. Falk Bräuning & Siem Jan Koopman, 2016. "The Dynamic Factor Network Model with an Application to Global Credit-Risk," Tinbergen Institute Discussion Papers 16-105/III, Tinbergen Institute.
    2. James Sampi, 2016. "High Dimensional Factor Models: An Empirical Bayes Approach," Working Papers 2016-75, Peruvian Economic Association.

    More about this item

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation

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