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Steady-state priors and Bayesian variable selection in VAR forecasting

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  • Louzis Dimitrios P.

    (Bank of Greece – Economic Analysis and Research Department, Athens, Greece)

Abstract

This study proposes methods for estimating Bayesian vector autoregressions (VARs) with a (semi-) 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

  • Louzis Dimitrios P., 2016. "Steady-state priors and Bayesian variable selection in VAR forecasting," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(5), pages 495-527, December.
    • Handle: RePEc:bpj:sndecm:v:20:y:2016:i:5:p:495-527:n:5
    DOI: 10.1515/snde-2015-0048
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    More about this item

    Keywords

    Bayesian VAR; macroeconomic forecasting; steadystates; variable selection;
    All these keywords.

    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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