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Forecasting Using Relative Entropy

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

Abstract

The paper describes a relative entropy procedure for imposing 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 not used in the construction of the original. The new distribution is informationally as close as possible to the original in the sense of minimizing the Kullback-Leibler Information Criterion, or relative entropy. We illustrate the technique with an example related to monetary policy that shows how to introduce restrictions from economic theory into a model's forecasts.

Suggested Citation

  • Robertson, John C & Tallman, Ellis W & Whiteman, Charles H, 2005. "Forecasting Using Relative Entropy," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 37(3), pages 383-401, June.
  • Handle: RePEc:mcb:jmoncb:v:37:y:2005:i:3:p:383-401
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    References listed on IDEAS

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    1. Geweke, John, 1989. "Bayesian Inference in Econometric Models Using Monte Carlo Integration," Econometrica, Econometric Society, vol. 57(6), pages 1317-1339, November.
    2. Hansen, Lars Peter & Singleton, Kenneth J, 1983. "Stochastic Consumption, Risk Aversion, and the Temporal Behavior of Asset Returns," Journal of Political Economy, University of Chicago Press, vol. 91(2), pages 249-265, April.
    3. Thomas Doan & Robert B. Litterman & Christopher A. Sims, 1983. "Forecasting and Conditional Projection Using Realistic Prior Distributions," NBER Working Papers 1202, National Bureau of Economic Research, Inc.
    4. Daniel F. Waggoner & Tao Zha, 1999. "Conditional Forecasts In Dynamic Multivariate Models," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 639-651, November.
    5. Robertson, John C & Tallman, Ellis W, 2001. "Improving Federal-Funds Rate Forecasts in VAR Models Used for Policy Analysis," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(3), pages 324-330, July.
    6. Charles L. Evans & Kenneth N. Kuttner, 1998. "Can VAR's describe monetary policy?," Working Paper Series WP-98-19, Federal Reserve Bank of Chicago.
    7. John C. Robertson & Ellis W. Tallman, 1999. "Vector autoregressions: forecasting and reality," Economic Review, Federal Reserve Bank of Atlanta, issue Q1, pages 4-18.
    8. Yuichi Kitamura & Michael Stutzer, 1997. "An Information-Theoretic Alternative to Generalized Method of Moments Estimation," Econometrica, Econometric Society, vol. 65(4), pages 861-874, July.
    9. Sims, Christopher A & Zha, Tao, 1998. "Bayesian Methods for Dynamic Multivariate Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 949-968, November.
    10. Rudebusch, Glenn D, 1998. "Do Measures of Monetary Policy in a VAR Make Sense?," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 907-931, November.
    11. Stutzer, Michael, 1996. " A Simple Nonparametric Approach to Derivative Security Valuation," Journal of Finance, American Finance Association, vol. 51(5), pages 1633-1652, December.
    12. Neely, Christopher J & Roy, Amlan & Whiteman, Charles H, 2001. "Risk Aversion versus Intertemporal Substitution: A Case Study of Identification Failure in the Intertemporal Consumption Capital Asset Pricing Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(4), pages 395-403, October.
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