Forecasting with Medium and Large Bayesian VARs
AbstractThis 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 nd 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.
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Bibliographic InfoPaper provided by Scottish Institute for Research in Economics (SIRE) in its series SIRE Discussion Papers with number 2011-38.
Date of creation: 2011
Date of revision:
Bayesian; Minnesota prior; stochastic search variable selection; predictive likelihood;
Other versions of this item:
- 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
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
This paper has been announced in the following NEP Reports:
- NEP-ALL-2012-06-05 (All new papers)
- NEP-ETS-2012-06-05 (Econometric Time Series)
- NEP-FOR-2012-06-05 (Forecasting)
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