Forecasting using relative entropy
AbstractThe 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.
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Bibliographic InfoPaper provided by Federal Reserve Bank of Atlanta in its series Working Paper with number 2002-22.
Date of creation: 2002
Date of revision:
Other versions of this item:
- NEP-ALL-2003-01-27 (All new papers)
- NEP-ETS-2003-01-27 (Econometric Time Series)
- NEP-RMG-2003-01-27 (Risk Management)
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