Business Intelligence

In-Sample Tests Of Predictive Ability: A New Approach

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Executive Summary

This paper presents analytical, Monte Carlo, and empirical evidence linking in-sample tests of predictive content and out-of-sample forecast accuracy. The authors' approach focuses on the negative effect that finite-sample estimation error has on forecast accuracy despite the presence of significant population-level predictive content. Specifically, they derive simple-to-use in-sample tests that test not only whether a particular variable has predictive content but also whether this content is estimated precisely enough to improve forecast accuracy. The tests are asymptotically non-central chi-square or non-central normal. They provide a convenient bootstrap method for computing the relevant critical values.

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