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Can out-of-sample forecast comparisons help prevent overfitting?

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  • Todd E. Clark

Abstract

This paper shows that out-of-sample forecast comparisons can help prevent data mining-induced overfitting. The basic results are drawn from simulations of a simple Monte Carlo design and a real data-based design similar to those in Lovell (1983) and Hoover and Perez (1999). In each simulation, a general-to-specific procedure is used to arrive at a model. If the selected specification includes any of the candidate explanatory variables, forecasts from the model are compared to forecasts from a benchmark model that is nested within the selected model. In particular, the competing forecasts are tested for equal MSE and encompassing. The simulations indicate most of the post-sample tests are roughly correctly sized, as long as just the in-sample portion of the data are used in model selection. Moreover, the tests have relatively good power, although some are consistently more powerful than others. The paper concludes with an application, modeling quarterly U.S. inflation.

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Bibliographic Info

Paper provided by Federal Reserve Bank of Kansas City in its series Research Working Paper with number RWP 00-05.

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Date of creation: 2000
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Handle: RePEc:fip:fedkrw:rwp00-05

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Keywords: Forecasting;

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References

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  1. Hans-Martin Krolzig & David Hendry, 1999. "Computer Automation of General-to-Specific Model Selection Procedures," Computing in Economics and Finance 1999 314, Society for Computational Economics.
  2. Harvey, David I & Leybourne, Stephen J & Newbold, Paul, 1998. "Tests for Forecast Encompassing," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 254-59, April.
  3. West, Kenneth D, 1996. "Asymptotic Inference about Predictive Ability," Econometrica, Econometric Society, vol. 64(5), pages 1067-84, September.
  4. David F. Hendry & Hans-Martin Krolzig, 1999. "Improving on 'Data mining reconsidered' by K.D. Hoover and S.J. Perez," Econometrics Journal, Royal Economic Society, vol. 2(2), pages 202-219.
  5. Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-63, July.
  6. Julia Campos & Neil R. Ericsson, 1999. "Contructive data mining: modeling consumers' expenditure in Venezuela," Econometrics Journal, Royal Economic Society, vol. 2(2), pages 226-240.
  7. Amano, Robert A. & van Norden, Simon, 1995. "Terms of trade and real exchange rates: the Canadian evidence," Journal of International Money and Finance, Elsevier, vol. 14(1), pages 83-104, February.
  8. Meese, Richard A. & Rogoff, Kenneth, 1983. "Empirical exchange rate models of the seventies : Do they fit out of sample?," Journal of International Economics, Elsevier, vol. 14(1-2), pages 3-24, February.
  9. Todd E. Clark & Michael W. McCracken, 2000. "Tests of Equal Forecast Accuracy and Encompassing for Nested Models," Econometric Society World Congress 2000 Contributed Papers 0319, Econometric Society.
  10. Bruce E. Hansen, 1999. "Discussion of 'Data mining reconsidered'," Econometrics Journal, Royal Economic Society, vol. 2(2), pages 192-201.
  11. Thomas Knox & James H. Stock & Mark W. Watson, 2000. "Empirical Bayes Forecasts of One Time Series Using Many Predictors," Econometric Society World Congress 2000 Contributed Papers 1421, Econometric Society.
  12. Martin D. D. Evans and Richard K. Lyons., 1999. "Order Flow and Exchange Rate Dynamics," Research Program in Finance Working Papers RPF-288, University of California at Berkeley.
  13. McCracken, Michael W., 2007. "Asymptotics for out of sample tests of Granger causality," Journal of Econometrics, Elsevier, vol. 140(2), pages 719-752, October.
  14. Halbert White, 2000. "A Reality Check for Data Snooping," Econometrica, Econometric Society, vol. 68(5), pages 1097-1126, September.
  15. Kevin Hoover & Stephen J. Perez, 2003. "Data Mining Reconsidered: Encompassing And The General-To-Specific Approach To Specification Search," Working Papers 9727, University of California, Davis, Department of Economics.
  16. repec:att:wimass:9710 is not listed on IDEAS
  17. James H. Stock & Mark W. Watson, 1998. "Diffusion Indexes," NBER Working Papers 6702, National Bureau of Economic Research, Inc.
  18. Ashley, R & Granger, C W J & Schmalensee, R, 1980. "Advertising and Aggregate Consumption: An Analysis of Causality," Econometrica, Econometric Society, vol. 48(5), pages 1149-67, July.
  19. West, Kenneth D & McCracken, Michael W, 1998. "Regression-Based Tests of Predictive Ability," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 817-40, November.
  20. Denton, Frank T, 1985. "Data Mining as an Industry," The Review of Economics and Statistics, MIT Press, vol. 67(1), pages 124-27, February.
  21. repec:cup:macdyn:v:5:y:2001:i:4:p:598-620 is not listed on IDEAS
  22. Chinn, Menzie D. & Meese, Richard A., 1995. "Banking on currency forecasts: How predictable is change in money?," Journal of International Economics, Elsevier, vol. 38(1-2), pages 161-178, February.
  23. Norman R. Swanson, 2000. "An Out of Sample Test for Granger Causality," Econometric Society World Congress 2000 Contributed Papers 0362, Econometric Society.
  24. Lovell, Michael C, 1983. "Data Mining," The Review of Economics and Statistics, MIT Press, vol. 65(1), pages 1-12, February.
  25. Timothy Cogley, 1998. "A simple adaptive measure of core inflation," Working Papers in Applied Economic Theory 98-06, Federal Reserve Bank of San Francisco.
  26. Granger, Clive W. J. & King, Maxwell L. & White, Halbert, 1995. "Comments on testing economic theories and the use of model selection criteria," Journal of Econometrics, Elsevier, vol. 67(1), pages 173-187, May.
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