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Forecasting using relative entropy

Author

Listed:
  • John C. Robertson
  • Ellis W. Tallman
  • Charles H. Whiteman

Abstract

The paper describes a relative entropy procedure for imposing moment restrictions on simulated forecast distributions from a variety of models. Starting from an empirical forecast distribution for some variables of interest, the technique generates a new empirical distribution that satisfies a set of moment restrictions. The new distribution is chosen to be as close as possible to the original in the sense of minimizing the associated Kullback-Leibler Information Criterion, or relative entropy. The authors illustrate the technique by using several examples that show how restrictions from other forecasts and from economic theory may be introduced into a model's forecasts.

Suggested Citation

  • John C. Robertson & Ellis W. Tallman & Charles H. Whiteman, 2002. "Forecasting using relative entropy," FRB Atlanta Working Paper 2002-22, Federal Reserve Bank of Atlanta.
    • Handle: RePEc:fip:fedawp:2002-22
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    References listed on IDEAS

    as
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