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Forecast accuracy after pretesting with an application to the stock market

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Author Info

  • Jan R. Magnus

    (CentER, Tilburg University, The Netherlands)

  • Dmitry Danilov

    (Eurandom, Eindhoven University of Technology, The Netherlands)

Abstract

In econometrics, as a rule, the same data set is used to select the model and, conditional on the selected model, to forecast. However, one typically reports the properties of the (conditional) forecast, ignoring the fact that its properties are affected by the model selection (pretesting). This is wrong, and in this paper we show that the error can be substantial. We obtain explicit expressions for this error. To illustrate the theory we consider a regression approach to stock market forecasting, and show that the standard predictions ignoring pretesting are much less robust than naive econometrics might suggest. We also propose a forecast procedure based on the 'neutral Laplace estimator', which leads to an improvement over standard model selection procedures. Copyright © 2004 John Wiley & Sons, Ltd.

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File URL: http://hdl.handle.net/10.1002/for.916
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Bibliographic Info

Article provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.

Volume (Year): 23 (2004)
Issue (Month): 4 ()
Pages: 251-274

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Handle: RePEc:jof:jforec:v:23:y:2004:i:4:p:251-274

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Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966

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References

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  1. Granger, C.W.J. & Pesaran, M. H., 1999. "Economic and Statistical Measures of Forecast Accuracy," Cambridge Working Papers in Economics 9910, Faculty of Economics, University of Cambridge.
  2. French, Kenneth R. & Schwert, G. William & Stambaugh, Robert F., 1987. "Expected stock returns and volatility," Journal of Financial Economics, Elsevier, vol. 19(1), pages 3-29, September.
  3. Pesaran, M.H. & Timmermann, A., 1992. "Forecasting Stock Returns," Cambridge Working Papers in Economics 9216, Faculty of Economics, University of Cambridge.
  4. Chen, Nai-Fu & Roll, Richard & Ross, Stephen A, 1986. "Economic Forces and the Stock Market," The Journal of Business, University of Chicago Press, vol. 59(3), pages 383-403, July.
  5. Fama, Eugene F. & French, Kenneth R., 1989. "Business conditions and expected returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 25(1), pages 23-49, November.
  6. Balvers, Ronald J & Cosimano, Thomas F & McDonald, Bill, 1990. " Predicting Stock Returns in an Efficient Market," Journal of Finance, American Finance Association, vol. 45(4), pages 1109-28, September.
  7. Lucas, Robert Jr, 1976. "Econometric policy evaluation: A critique," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 1(1), pages 19-46, January.
  8. Abadir, K.M. & Magnus, J.R., 2001. "Notation in Econometrics: A Proposal for a Standard," Discussion Paper 2001-8, Tilburg University, Center for Economic Research.
  9. Danilov, D.L. & Magnus, J.R., 2001. "On the Harm that Pretesting Does," Discussion Paper 2001-37, Tilburg University, Center for Economic Research.
  10. Fama, Eugene F & French, Kenneth R, 1992. " The Cross-Section of Expected Stock Returns," Journal of Finance, American Finance Association, vol. 47(2), pages 427-65, June.
  11. Jan R. Magnus & J. Durbin, 1999. "Estimation of Regression Coefficients of Interest When Other Regression Coefficients Are of No Interest," Econometrica, Econometric Society, vol. 67(3), pages 639-644, May.
  12. Pesaran, M Hashem & Timmermann, Allan, 1995. " Predictability of Stock Returns: Robustness and Economic Significance," Journal of Finance, American Finance Association, vol. 50(4), pages 1201-28, September.
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Cited by:
  1. Joshua Gallin & Randal Verbrugge, 2007. "Improving the CPI’s Age-Bias Adjustment: Leverage, Disaggregation and Model Averaging," Working Papers 411, U.S. Bureau of Labor Statistics.
  2. Magnus, Jan R. & Wan, Alan T.K. & Zhang, Xinyu, 2011. "Weighted average least squares estimation with nonspherical disturbances and an application to the Hong Kong housing market," Computational Statistics & Data Analysis, Elsevier, vol. 55(3), pages 1331-1341, March.
  3. Magnus, Jan & Peresetsky, Anatoly, 2010. "The price of Moscow apartments," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 17(1), pages 89-105.
  4. Mouchart, Michel & Rombouts, Jeroen V.K., 2005. "Clustered panel data models: an efficient approach for nowcasting from poor data," International Journal of Forecasting, Elsevier, vol. 21(3), pages 577-594.
  5. Ouysse, Rachida, 2006. "Consistent variable selection in large panels when factors are observable," Journal of Multivariate Analysis, Elsevier, vol. 97(4), pages 946-984, April.
  6. Magnus, J.R. & Wang, W. & Zhang, Xinyu, 2012. "WALS Prediction," Discussion Paper 2012-043, Tilburg University, Center for Economic Research.

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