Published in:Federal Reserve Bank of Kansas City, Economic Research Department Research Working Paper ; No. 2009-051A
Extent:
1 Online-Ressource (36 p)
Language:
English
DOI:
10.2139/ssrn.1457422
Identifier:
Origination:
Footnote:
Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments July 2009 erstellt
Description:
This paper presents analytical, Monte Carlo, and empirical evidence linking in-sample tests of predictive content and out-of-sample forecast accuracy. Our 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, we 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. Our tests are asymptotically non-central chi-square or non-central normal. We provide a convenient bootstrap method for computing the relevant critical values. In the Monte Carlo and empirical analysis, we compare the effectiveness of our testing procedure with more common testing procedures