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Empirical Bayesian density forecasting in Iowa and shrinkage for the Monte Carlo era

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  • Lewis, Kurt F.
  • Whiteman, Charles H.

Abstract

The track record of a sixteen-year history of density forecasts of state tax revenue in Iowa is studied, and potential improvements sought through a search for better performing "priors" similar to that conducted two decades ago for point forecasts by Doan, Litterman, and Sims (Econometric Reviews, 1984). Comparisons of the point- and density-forecasts produced under the flat prior are made to those produced by the traditional (mixed estimation) "Bayesian VAR" methods of Doan, Litterman, and Sims, as well as to fully Bayesian, "Minnesota Prior" forecasts. The actual record, and to a somewhat lesser extent, the record of the alternative procedures studied in pseudo-real-time forecasting experiments, share a characteristic: subsequently realized revenues are in the lower tails of the predicted distributions "too often". An alternative empirically-based prior is found by working directly on the probability distribution for the VAR parameters, seeking a betterperforming entropically tilted prior that minimizes in-sample mean-squared-error subject to a Kullback-Leibler divergence constraint that the new prior not differ "too much" from the original. We also study the closely related topic of robust prediction appropriate for situations of ambiguity. Robust "priors" are competitive in out-of-sample forecasting; despite the freedom afforded the entropically tilted prior, it does not perform better than the simple alternatives. --

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Bibliographic Info

Paper provided by Deutsche Bundesbank, Research Centre in its series Discussion Paper Series 1: Economic Studies with number 2006,28.

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Date of creation: 2006
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Handle: RePEc:zbw:bubdp1:4757

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Cited by:
  1. Carriero, Andrea & Clark, Todd & Marcellino, Massimiliano, 2011. "Bayesian VARs: Specification Choices and Forecast Accuracy," CEPR Discussion Papers 8273, C.E.P.R. Discussion Papers.
  2. Geweke, John & Amisano, Gianni, 2010. "Comparing and evaluating Bayesian predictive distributions of asset returns," International Journal of Forecasting, Elsevier, vol. 26(2), pages 216-230, April.

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