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Empirical Bayes Methods for Dynamic Factor Models

Author

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  • Siem Jan Koopman
  • Geert Mesters

    (VU University Amsterdam)

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.

Suggested Citation

  • Siem Jan Koopman & Geert Mesters, 2014. "Empirical Bayes Methods for Dynamic Factor Models," Tinbergen Institute Discussion Papers 14-061/III, Tinbergen Institute.
  • Handle: RePEc:tin:wpaper:20140061
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. James Sampi, 2016. "High Dimensional Factor Models: An Empirical Bayes Approach," Working Papers 2016-75, Peruvian Economic Association.
    2. 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.

    More about this item

    Keywords

    Importance sampling; Kalman filtering; Likelihood-based analysis; Posterior modes; Rao-Blackwellization; Shrinkage;

    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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