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VAR Forecasting Using Bayesian Variable Selection

Author

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  • Dimitris Korobilis

    (Université Catholique de Louvain; The Rimini Centre for Economic Analysis (RCEA))

Abstract

This paper develops methods for automatic selection of variables in Bayesian vector autoregressions (VARs) using the Gibbs sampler. In particular, I provide computationally efficient algorithms for stochastic variable selection in generic linear and nonlinear models, as well as models of large dimensions. The performance of the proposed variable selection method is assessed in forecasting three major macroeconomic time series of the UK economy. Data-based restrictions of VAR coefficients can help improve upon their unrestricted counterparts in forecasting, and in many cases they compare favorably to shrinkage estimators.

Suggested Citation

  • Dimitris Korobilis, 2010. "VAR Forecasting Using Bayesian Variable Selection," Working Paper series 51_10, Rimini Centre for Economic Analysis, revised Apr 2011.
  • Handle: RePEc:rim:rimwps:51_10
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    File URL: http://www.rcea.org/RePEc/pdf/wp51_10.pdf
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    References listed on IDEAS

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    More about this item

    Keywords

    Forecasting; variable selection; time-varying parameters; Bayesian vector autoregression;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • 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
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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