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Forecasting with Medium and Large Bayesian VARs

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  • Gary Koop

    (University of Strathclyde; The Rimini Centre for Economic Analysis (RCEA))

Abstract

This paper is motivated by the recent interest in the use of Bayesian VARs for forecasting, even in cases where the number of dependent variables is large. In such cases, factor methods have been traditionally used but recent work using a particular prior suggests that Bayesian VAR methods can forecast better. In this paper, we consider a range of alternative priors which have been used with small VARs, discuss the issues which arise when they are used with medium and large VARs and examine their forecast performance using a US macroeconomic data set containing 168 variables. We find that Bayesian VARs do tend to forecast better than factor methods and provide an extensive comparison of the strengths and weaknesses of various approaches. Our empirical results show the importance of using forecast metrics which use the entire predictive density, instead of using only point forecasts.

Suggested Citation

  • Gary Koop, 2010. "Forecasting with Medium and Large Bayesian VARs," Working Paper series 43_10, Rimini Centre for Economic Analysis.
    • Handle: RePEc:rim:rimwps:43_10
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    References listed on IDEAS

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    More about this item

    Keywords

    Bayesian; Minnesota prior; stochastic search variable selection; predictive likelihood;
    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
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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