Approximately normal tests for equal predictive accuracy in nested models

doi:10.1016/j.jeconom.2006.05.023

Return

Approximately normal tests for equal predictive accuracy in nested models

2007-05-01

1.4K

Todd E. Clark *

Kenneth D. West

DOI：10.1016/j.jeconom.2006.05.023

Original

Save

View PDF

Abstract

En 中文

Forecast evaluation often compares a parsimonious null model to a larger model that nests the null model. Under the null that the parsimonious model generates the data, the larger model introduces noise into its forecasts by estimating parameters whose population values are zero. We observe that the mean squared prediction error (MSPE) from the parsimonious model is therefore expected to be smaller than that of the larger model. We describe how to adjust MSPEs to account for this noise. We propose applying standard methods [West, K.D., 1996. Asymptotic inference about predictive ability. Econometrica 64, 1067-1084] to test whether the adjusted mean squared error difference is zero. We refer to nonstandard limiting distributions derived in Clark and McCracken [2001. Tests of equal forecast accuracy and encompassing for nested models. Journal of Econometrics 105, 85-110; 2005a. Evaluating direct multistep forecasts. Econometric Reviews 24, 369-404] to argue that use of standard normal critical values will yield actual sizes close to, but a little less than, nominal size. Simulation evidence supports our recommended procedure. (c) 2006 Elsevier B.V. All rights reserved.

Keywords:

out of sample

causality

random walk

testing

efficient markets

principle of parsimony

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.