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Conditional Forecasting For The U.S. Dairy Price Complex With A Bayesian Vector Autoregressive Model

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  • Thraen, Cameron S.
  • Thompson, Stanley R.
  • Gohout, Wolfgang

Abstract

A dynamic Bayesian Vector Autoregressive model of the U.S. dairy price complex is estimated based on the Normal-Wishart distribution. The Gibbs sample technique is use with the Normal-Wishart distribution to provide conditional forecasts on the future time-paths of the model variables. The conditional forecasts for key prices are examined. Confidence intervals are calculated for the conditional forecasts.

Suggested Citation

  • Thraen, Cameron S. & Thompson, Stanley R. & Gohout, Wolfgang, 2002. "Conditional Forecasting For The U.S. Dairy Price Complex With A Bayesian Vector Autoregressive Model," 2002 Annual meeting, July 28-31, Long Beach, CA 19706, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    • Handle: RePEc:ags:aaea02:19706
    DOI: 10.22004/ag.econ.19706
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    References listed on IDEAS

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    1. 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.
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    Keywords

    Demand and Price Analysis;

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