We 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. We show that standard regression statistics generally fail to account for error introduced by estimation of these parameters. We propose computationally convenient test statistics that properly account for such error. Simulations indicate that the procedures can work well in samples of size typically available, although there sometimes are substantial size distortions.
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Paper provided by National Bureau of Economic Research, Inc in its series NBER Technical Working Papers with number
0226.
Length: Date of creation: Mar 1998 Date of revision: Handle: RePEc:nbr:nberte:0226
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Find related papers by JEL classification: C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation and Testing C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications
References listed on IDEAS Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
Jacob A. Mincer & Victor Zarnowitz, 1969.
"The Evaluation of Economic Forecasts,"
NBER Chapters,
in: Economic Forecasts and Expectations: Analysis of Forecasting Behavior and Performance, pages 1-46
National Bureau of Economic Research, Inc.
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