Abstract
Language | English |
---|---|
Place of Publication | Glasgow |
Publisher | University of Strathclyde |
Pages | 1-34 |
Number of pages | 35 |
Volume | 2011 |
Publication status | Published - Feb 2010 |
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Keywords
- bayesian
- Minnesota prior
- stochastic search variable selection
- predictive likelihood
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Forecasting with Medium and Large Bayesian VARS. / Koop, Gary.
17. ed. Glasgow : University of Strathclyde, 2010. p. 1-34.Research output: Working paper › Discussion paper
TY - UNPB
T1 - Forecasting with Medium and Large Bayesian VARS
AU - Koop, Gary
N1 - Discussion paper.
PY - 2010/2
Y1 - 2010/2
N2 - 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.
AB - 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.
KW - bayesian
KW - Minnesota prior
KW - stochastic search variable selection
KW - predictive likelihood
M3 - Discussion paper
VL - 2011
SP - 1
EP - 34
BT - Forecasting with Medium and Large Bayesian VARS
PB - University of Strathclyde
CY - Glasgow
ER -