The authors develop regression-based tests of hypotheses about out of sample prediction errors. Representative tests include ones for zero mean and zero correlation between a prediction error and a vector of predictors. The relevant environments are ones in which predictions depend on estimated parameters. The authors show that standard regression statistics generally fail to account for errors introduced by estimation of these parameters. They propose computationally convenient test statistics that properly account for such errors. Simulations indicate that the procedures can work well in samples of size typically available, although there sometimes are substantial size distortions. Copyright 1998 by Economics Department of the University of Pennsylvania and the Osaka University Institute of Social and Economic Research Association.
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Article provided by Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association in its journal International Economic Review.
Volume (Year): 39 (1998) Issue (Month): 4 (November) Pages: 817-40 Download reference. The following formats are available: HTML,
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