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Numerical Methods for Estimation and Inference in Bayesian VAR-Models

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Listed:
  • Kadiyala, K Rao
  • Karlsson, Sune

Abstract

In Bayesian analysis of vector autoregressive models, and especially in forecasting applications, the Minnesota prior of Litterman is frequently used. In many cases other prior distributions provided better forecasts and are preferable from a theoretical standpoint. Several of these priors require numerical methods in order to evaluate the posterior distribution. Different ways of implementing Monte Carlo integration are considered. It is found that Gibbs sampling performs as well as, or better, then importance sampling and that the Gibbs sampling algorithms are less adversely affected by model size. We also report on the forecasting performance of the different prior distributions

Suggested Citation

  • 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.
  • Handle: RePEc:jae:japmet:v:12:y:1997:i:2:p:99-132
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    File URL: http://qed.econ.queensu.ca:80/jae/1997-v12.2/
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    References listed on IDEAS

    as
    1. Shorrocks, A F, 1980. "The Class of Additively Decomposable Inequality Measures," Econometrica, Econometric Society, vol. 48(3), pages 613-625, April.
    2. Bourguignon, Francois, 1979. "Decomposable Income Inequality Measures," Econometrica, Econometric Society, vol. 47(4), pages 901-920, July.
    3. Gastwirth, Joseph L, 1974. "Large Sample Theory of Some Measures of Income Inequality," Econometrica, Econometric Society, vol. 42(1), pages 191-196, January.
    4. Cowell, Frank A., 1989. "Sampling variance and decomposable inequality measures," Journal of Econometrics, Elsevier, pages 27-41.
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    More about this item

    JEL classification:

    • 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; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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