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 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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Paper provided by Tilburg University, Center for Economic Research in its series Discussion Paper with number
76.
Find related papers by JEL classification: C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
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