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In-sample tests of predictive ability: a new approach

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  • Todd E. Clark
  • Michael W. McCracken

Abstract

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.

Suggested Citation

  • Todd E. Clark & Michael W. McCracken, 2009. "In-sample tests of predictive ability: a new approach," Working Papers 2009-051, Federal Reserve Bank of St. Louis.
  • Handle: RePEc:fip:fedlwp:2009-051
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Manuel Lukas & Eric Hillebrand, 2014. "Bagging Weak Predictors," CREATES Research Papers 2014-01, Department of Economics and Business Economics, Aarhus University.
    2. Calhoun, Gray, 2014. "Out-Of-Sample Comparisons of Overfit Models," Staff General Research Papers Archive 32462, Iowa State University, Department of Economics.
    3. Chauvet, Marcelle & Senyuz, Zeynep & Yoldas, Emre, 2015. "What does financial volatility tell us about macroeconomic fluctuations?," Journal of Economic Dynamics and Control, Elsevier, vol. 52(C), pages 340-360.
    4. Tom Boot & Andreas Pick, 2017. "A near optimal test for structural breaks when forecasting under square error loss," Tinbergen Institute Discussion Papers 17-039/III, Tinbergen Institute.
    5. Hossein Hassani & Emmanuel Sirimal Silva, 2015. "A Kolmogorov-Smirnov Based Test for Comparing the Predictive Accuracy of Two Sets of Forecasts," Econometrics, MDPI, Open Access Journal, vol. 3(3), pages 1-20, August.
    6. Juan Antolin-Diaz & Thomas Drechsel & Ivan Petrella, 2017. "Tracking the Slowdown in Long-Run GDP Growth," The Review of Economics and Statistics, MIT Press, vol. 99(2), pages 343-356, May.
    7. 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, pages 78-93.
    8. Antolin-Diaz, Juan & Drechsel, Thomas & Petrella, Ivan, 2014. "Following the Trend: Tracking GDP when Long-Run Growth is Uncertain," CEPR Discussion Papers 10272, C.E.P.R. Discussion Papers.
    9. Hsiu-Hsin Ko, 2016. "Exchange Rate Predictability in Finite Samples," The Japanese Economic Review, Japanese Economic Association, vol. 67(3), pages 361-378, September.
    10. Pincheira, Pablo & Selaive, Jorge & Nolazco, Jose Luis, 2017. "Forecasting Inflation in Latin America with Core Measures," MPRA Paper 80496, University Library of Munich, Germany.

    More about this item

    Keywords

    Economic forecasting;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General

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