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Forecasting Using Relative Entropy

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Author Info

  • 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.

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Bibliographic Info

Article provided by Blackwell Publishing in its journal Journal of Money, Credit and Banking.

Volume (Year): 37 (2005)
Issue (Month): 3 (June)
Pages: 383-401

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Handle: RePEc:mcb:jmoncb:v:37:y:2005:i:3:p:383-401

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Web page: http://www.blackwellpublishing.com/journal.asp?ref=0022-2879

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References

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Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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  1. Glenn D. Rudebusch, 1996. "Do measures of monetary policy in a VAR make sense?," Working Papers in Applied Economic Theory 96-05, Federal Reserve Bank of San Francisco.
  2. 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.
  3. Geweke, John, 1989. "Bayesian Inference in Econometric Models Using Monte Carlo Integration," Econometrica, Econometric Society, vol. 57(6), pages 1317-39, November.
  4. Stutzer, Michael, 1996. " A Simple Nonparametric Approach to Derivative Security Valuation," Journal of Finance, American Finance Association, vol. 51(5), pages 1633-52, December.
  5. John C. Robertson & Ellis W. Tallman, 1999. "Vector autoregressions: forecasting and reality," Economic Review, Federal Reserve Bank of Atlanta, issue Q1, pages 4-18.
  6. Charles Evans & Kenneth Kuttner, 1998. "Can VARs describe monetary policy?," Research Paper 9812, Federal Reserve Bank of New York.
  7. 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 324-30, July.
  8. Thomas Doan & Robert B. Litterman & Christopher A. Sims, 1986. "Forecasting and conditional projection using realistic prior distribution," Staff Report 93, Federal Reserve Bank of Minneapolis.
  9. Christopher A. Sims & Tao Zha, 1996. "Bayesian methods for dynamic multivariate models," Working Paper 96-13, Federal Reserve Bank of Atlanta.
  10. repec:wop:humbsf:1999-4 is not listed on IDEAS
  11. 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.
  12. 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 249-65, April.
  13. 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.
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Citations

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Cited by:
  1. Altavilla, Carlo & Giacomini, Raffaella & Ragusa, Giuseppe, 2014. "Anchoring the yield curve using survey expectations," Working Paper Series 1632, European Central Bank.
  2. Karlsson, Sune, 2012. "Forecasting with Bayesian Vector Autoregressions," Working Papers 2012:12, Örebro University, School of Business.
  3. Eric Leeper, 2003. "An "Inflation Reports" Report," NBER Working Papers 10089, National Bureau of Economic Research, Inc.
  4. Harrison, Richard & Taylor, Tim, 2012. "Non-rational expectations and the transmission mechanism," Bank of England working papers 448, Bank of England.
  5. Andrew P Blake & Haroon Mumtaz, 2012. "Applied Bayesian econometrics for central bankers," Technical Books, Centre for Central Banking Studies, Bank of England, edition 1, number 4.
  6. Cogley, Timothy W. & Morozov, Sergei & Sargent, Thomas J., 2003. "Bayesian fan charts for UK inflation: Forecasting and sources of uncertainty in an evolving monetary system," CFS Working Paper Series 2003/44, Center for Financial Studies (CFS).
  7. Michal Franta & Jozef Barunik & Roman Horvath & Katerina Smidkova, 2011. "Are Bayesian Fan Charts Useful for Central Banks? Uncertainty, Forecasting, and Financial Stability Stress Tests," Working Papers 2011/10, Czech National Bank, Research Department.
  8. Chalabi, Yohan & Wuertz, Diethelm, 2012. "Portfolio optimization based on divergence measures," MPRA Paper 43332, University Library of Munich, Germany.
  9. Walker, Todd B & Haley, M. Ryan, 2009. "Alternative Tilts for Nonparametric Option Pricing," MPRA Paper 17140, University Library of Munich, Germany.
  10. Manzan, Sebastiano & Zerom, Dawit, 2009. "Are Macroeconomic Variables Useful for Forecasting the Distribution of U.S. Inflation?," MPRA Paper 14387, University Library of Munich, Germany.
  11. Carlo Altavilla & Raffaella Giacomini & Riccardo Costantini, 2013. "Bond returns and market expectations," CeMMAP working papers CWP20/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  12. Smimou, K. & Bector, C.R. & Jacoby, G., 2007. "A subjective assessment of approximate probabilities with a portfolio application," Research in International Business and Finance, Elsevier, vol. 21(2), pages 134-160, June.
  13. Haley, M. Ryan & McGee, M. Kevin, 2011. ""KLICing" there and back again: Portfolio selection using the empirical likelihood divergence and Hellinger distance," Journal of Empirical Finance, Elsevier, vol. 18(2), pages 341-352, March.
  14. Kenneth Beauchemin & Saeed Zaman, 2011. "A medium scale forecasting model for monetary policy," Working Paper 1128, Federal Reserve Bank of Cleveland.
  15. Hall, Stephen G. & Mitchell, James, 2007. "Combining density forecasts," International Journal of Forecasting, Elsevier, vol. 23(1), pages 1-13.
  16. Shaun P Vahey & Elizabeth C Wakerly, 2013. "Moving towards probability forecasting," BIS Papers chapters, in: Bank for International Settlements (ed.), Globalisation and inflation dynamics in Asia and the Pacific, volume 70, pages 3-8 Bank for International Settlements.
  17. Marco Del Negro & Frank Schorfheide, 2012. "DSGE model-based forecasting," Staff Reports 554, Federal Reserve Bank of New York.
  18. Salois, Matthew & Moss, Charles, 2010. "An Information Approach to the Dynamics in Farm Income: Implications for Farmland Markets," MPRA Paper 26850, University Library of Munich, Germany.

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