Forecast accuracy after pretesting with an application to the stock market

Forecast accuracy after pretesting with an application to the stock market

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Article ID: iaor200838
Country: United Kingdom
Volume: 23
Issue: 4
Start Page Number: 251
End Page Number: 274
Publication Date: Jul 2004
Journal: International Journal of Forecasting
Authors: ,
Keywords: financial
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.

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