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Prévoir sans persistance

  • Christophe Boucher


    (Axe Finance - CES - Centre d'économie de la Sorbonne - UP1 - Université Panthéon-Sorbonne - CNRS - A.A.Advisors-QCG - ABN AMRO)

  • Bertrand Maillet


    (A.A.Advisors-QCG - ABN AMRO, LEO - Laboratoire d'économie d'Orleans - UO - Université d'Orléans - CNRS, EIF - Europlace Institute of Finance)

The forecasting literature has identified three important and broad issues : the predictive content is unstable over time, in-sample and out-of-sample discordant results and the problematic statistical inference with highly persistent predictors. In this paper, we simultaneously address these three issues, proposing to directly treat the persistence of forecasting variables before use. We thus cut-out the low frequency components and show, in simulations and on financial data, that this pre-treatment improves the predictive power of the studied economic variables.

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Paper provided by HAL in its series Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) with number halshs-00662771.

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Date of creation: Jan 2012
Date of revision:
Publication status: Published in Documents de travail du Centre d'Economie de la Sorbonne 2012.01 - ISSN : 1955-611X. 2012
Handle: RePEc:hal:cesptp:halshs-00662771
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  1. Christophe Boucher & Bertrand Maillet, 2011. "Une analyse temps-fréquences des cycles financiers," Revue économique, Presses de Sciences-Po, vol. 62(3), pages 441-450.
  2. Todd E. Clark & Michael McCracken, 1999. "Tests of Equal Forecast Accuracy and Encompassing for Nested Models," Computing in Economics and Finance 1999 1241, Society for Computational Economics.
  3. Michael Jansson & Marcelo J. Moreira, 2004. "Optimal Inference in Regression Models with Nearly Integrated Regressors," NBER Technical Working Papers 0303, National Bureau of Economic Research, Inc.
  4. Campbell, John & Yogo, Motohiro, 2006. "Efficient tests of stock return predictability," Scholarly Articles 3122601, Harvard University Department of Economics.
  5. Cavanagh, Christopher L. & Elliott, Graham & Stock, James H., 1995. "Inference in Models with Nearly Integrated Regressors," Econometric Theory, Cambridge University Press, vol. 11(05), pages 1131-1147, October.
  6. Pesaran, M. Hashem & Timmermann, Allan, 2007. "Selection of estimation window in the presence of breaks," Journal of Econometrics, Elsevier, vol. 137(1), pages 134-161, March.
  7. Martin Lettau & Stijn Van Nieuwerburgh, 2006. "Reconciling the Return Predictability Evidence," 2006 Meeting Papers 29, Society for Economic Dynamics.
  8. Wayne E. Ferson & Sergei Sarkissian & Timothy Simin, 2002. "Spurious Regressions in Financial Economics?," NBER Working Papers 9143, National Bureau of Economic Research, Inc.
  9. Nelson, Charles R & Kim, Myung J, 1993. " Predictable Stock Returns: The Role of Small Sample Bias," Journal of Finance, American Finance Association, vol. 48(2), pages 641-61, June.
  10. Campbell, John & Shiller, Robert, 1988. "Stock Prices, Earnings, and Expected Dividends," Scholarly Articles 3224293, Harvard University Department of Economics.
  11. Andrew Ang & Geert Bekaert, 2001. "Stock Return Predictability: Is it There?," NBER Working Papers 8207, National Bureau of Economic Research, Inc.
  12. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
  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. Walter Torous & Rossen Valkanov & Shu Yan, 2004. "On Predicting Stock Returns with Nearly Integrated Explanatory Variables," The Journal of Business, University of Chicago Press, vol. 77(4), pages 937-966, October.
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