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Steady-state priors and Bayesian variable selection in VAR forecasting

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  • Dimitrios P. Louzis

    () (Bank of Greece)

Abstract

This study proposes methods for estimating Bayesian vector autoregressions (VARs) with an automatic variable selection and an informative prior on the unconditional mean or steady-state of the system. We show that extant Gibbs sampling methods for Bayesian variable selection can be efficiently extended to incorporate prior beliefs on the steady-state of the economy. Empirical analysis, based on three major US macroeconomic time series, indicates that the out-of-sample forecasting accuracy of a VAR model is considerably improved when it combines both variable selection and steady-state prior information.

Suggested Citation

  • Dimitrios P. Louzis, 2015. "Steady-state priors and Bayesian variable selection in VAR forecasting," Working Papers 195, Bank of Greece.
  • Handle: RePEc:bog:wpaper:195
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    References listed on IDEAS

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    1. Dimitris Korobilis, 2013. "Var Forecasting Using Bayesian Variable Selection," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(2), pages 204-230, March.
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    6. Peter Reinhard Hansen & Asger Lunde & James M. Nason, 2003. "Choosing the Best Volatility Models: The Model Confidence Set Approach," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 65(s1), pages 839-861, December.
    7. Karlsson, Sune, 2013. "Forecasting with Bayesian Vector Autoregression," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 791-897, Elsevier.
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    1. repec:spr:empeco:v:53:y:2017:i:4:d:10.1007_s00181-016-1175-4 is not listed on IDEAS

    More about this item

    Keywords

    Bayesian VAR; Steady states; Variable selection; Macroeconomic forecasting;

    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

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