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Forecast accuracy after pretesting with an application to the stock market

✍ Scribed by Dmitry Danilov; Jan R. Magnus

Publisher
John Wiley and Sons
Year
2004
Tongue
English
Weight
203 KB
Volume
23
Category
Article
ISSN
0277-6693

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✦ Synopsis

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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