Forecast Accuracy after Pretesting with an Application to the Stock Market
AbstractIn 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 very substantial.We obtain explicit expressions for this error.To illustrate the theory we consider the regression approach of Pesaran and Timmermann (1994) to stock market forecasting, and show that their proposed recursive predictions are much less robust than naive econometrics might suggest.
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Bibliographic InfoPaper provided by Tilburg University, Center for Economic Research in its series Discussion Paper with number 2002-76.
Date of creation: 2002
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forecasting; stock markets; return on investment;
Other versions of this item:
- Jan R. Magnus & Dmitry Danilov, 2004. "Forecast accuracy after pretesting with an application to the stock market," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(4), pages 251-274.
- 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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- Magnus, J.R. & Wang, W. & Zhang, Xinyu, 2012. "WALS Prediction," Discussion Paper 2012-043, Tilburg University, Center for Economic Research.
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