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In-sample tests of predictive ability: A new approach

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  • Clark, Todd E.
  • McCracken, Michael W.

Abstract

This paper presents 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. We derive in-sample tests that assess whether a variable has predictive content and 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 for computing critical values. In Monte Carlo and empirical analysis, we examine the effectiveness of our testing procedure.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Econometrics.

Volume (Year): 170 (2012)
Issue (Month): 1 ()
Pages: 1-14

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Handle: RePEc:eee:econom:v:170:y:2012:i:1:p:1-14

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Web page: http://www.elsevier.com/locate/jeconom

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Keywords: Predictability; Forecast accuracy; In-sample;

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References

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Cited by:
  1. Lutz Kilian & Robert J. Vigfusson, 2013. "Do Oil Prices Help Forecast U.S. Real GDP? The Role of Nonlinearities and Asymmetries," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(1), pages 78-93, January.
  2. Marcelle Chauvet & Zeynep Senyuz & Emre Yoldas, 2012. "What does financial volatility tell us about macroeconomic fluctuations?," Finance and Economics Discussion Series 2012-09, Board of Governors of the Federal Reserve System (U.S.).
  3. Calhoun, Gray, 2014. "Out-Of-Sample Comparisons of Overfit Models," Staff General Research Papers 32462, Iowa State University, Department of Economics.
  4. Manuel Lukas & Eric Hillebrand, 2014. "Bagging Weak Predictors," CREATES Research Papers 2014-01, School of Economics and Management, University of Aarhus.

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