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Forecast Accuracy after Pretesting with an Application to the Stock Market

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  • Danilov, D.L.

    (Tilburg University, School of Economics and Management)

  • Magnus, J.R.

    (Tilburg University, School of Economics and Management)

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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(This abstract was borrowed from another version of this item.)
(This abstract was borrowed from another version of this item.)
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Danilov, D.L. & Magnus, J.R., 2002. "Forecast Accuracy after Pretesting with an Application to the Stock Market," Other publications TiSEM cb9b9b63-40a9-4035-924e-d, Tilburg University, School of Economics and Management.
    • Handle: RePEc:tiu:tiutis:cb9b9b63-40a9-4035-924e-d1fc04a994e1
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    References listed on IDEAS

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    Cited by:

    1. Jan R. Magnus & Wendun Wang & Xinyu Zhang, 2016. "Weighted-Average Least Squares Prediction," Econometric Reviews, Taylor & Francis Journals, vol. 35(6), pages 1040-1074, June.
    2. Magnus, Jan & Peresetsky, Anatoly, 2010. "The price of Moscow apartments," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 17(1), pages 89-105.
    3. 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.
    4. 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.
    5. 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.
    6. Ali Mehrabani & Aman Ullah, 2022. "Weighted Average Estimation in Panel Data," Working Papers 202209, University of California at Riverside, Department of Economics, revised Apr 2022.
    7. 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.

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    More about this item

    JEL classification:

    • C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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