Numerical Aspects of Bayesian VAR-modeling
AbstractIn Bayesian analysis of VAR-models, and especially in forecasting applications, the Minnesota prior of Litterman is frequently used. In many cases other prior distributions provide better forecasts and are preferable from a theoretical standpoint. This paper considers the numerical procedures needed to implement these prior distributions. In addition we also report on the forecasting performance of the different prior distributions considered in the paper.
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Bibliographic InfoPaper provided by Stockholm School of Economics in its series Working Paper Series in Economics and Finance with number 12.
Length: 43 pages
Date of creation: Mar 1994
Date of revision:
Publication status: Published in Journal of Applied Econometrics, 1997, pages 99-132
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Postal: The Economic Research Institute, Stockholm School of Economics, P.O. Box 6501, 113 83 Stockholm, Sweden
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Monte Carlo integration; importance sampling; Gibbs sampling; antithetic variates; forecasting;
Other versions of this item:
- Kadiyala, K Rao & Karlsson, Sune, 1997. "Numerical Methods for Estimation and Inference in Bayesian VAR-Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(2), pages 99-132, March-Apr.
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
- 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-1998-11-05 (All new papers)
- NEP-CMP-1998-11-11 (Computational Economics)
- NEP-ETS-1998-11-05 (Econometric Time Series)
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