In-sample tests of predictive ability: a new approach
AbstractThis 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.
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Bibliographic InfoPaper provided by Federal Reserve Bank of St. Louis in its series Working Papers with number 2009-051.
Date of creation: 2009
Date of revision:
Other versions of this item:
- Todd E. Clark & Michael W. McCracken, 2009. "In-sample tests of predictive ability: a new approach," Research Working Paper RWP 09-10, Federal Reserve Bank of Kansas City.
- NEP-ALL-2009-10-31 (All new papers)
- NEP-CBA-2009-10-31 (Central Banking)
- NEP-ECM-2009-10-31 (Econometrics)
- NEP-FOR-2009-10-31 (Forecasting)
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- Calhoun, Gray, 2012. "Out-Of-Sample Comparisons of Overfit Models," Staff General Research Papers 32462, Iowa State University, Department of Economics.
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