We enhance the theory of asymptotic inference about predictive ability by considering the case when a set of variables used to construct predictions is sizable. To this end, we consider an alternative asymptotic framework where the number of predictors tends to in nity with the sample size, although more slowly. Depending on the situation the asymptotic normal distribution of an average prediction criterion either gains additional variance as in the few predictors case, or gains non-zero bias which has no analogs in the few predictors case. By properly modifying conventional test statistics it is possible to remove most size distortions when there are many predictors, and improve test sizes even when there are few of them.
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Paper provided by Center for Economic and Financial Research (CEFIR) in its series Working Papers with number
w0096.
Length: 43 pages Date of creation: Jan 2007 Date of revision: Handle: RePEc:cfr:cefirw:w0096
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West, Kenneth D & McCracken, Michael W, 1998.
"Regression-Based Tests of Predictive Ability,"
International Economic Review,
Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 817-40, November.
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