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Recursive Predictability Tests for Real-Time Data


  • Rossi, Barbara
  • Inoue, Atsushi


We propose a sequential test for predictive ability. The test is designed for recursive regressions in which the researcher is interested in recursively assessing whether some economic variables have predictive or explanatory content for another variable. It is common in the forecasting literature to assess predictive ability by using "one-shot" tests at each estimation period. We show that this practice: (i) leads to size distortions; (ii) selects overfitted models and provides spurious evidence of in-sample predictive ability; (iii) may lower the accuracy of the model selected by the test. The usefulness of the proposed test is shown in well-know empirical applications to the real-time predictive content of money for output, and the selection between linear and non-linear models.

Suggested Citation

  • Rossi, Barbara & Inoue, Atsushi, 2003. "Recursive Predictability Tests for Real-Time Data," Working Papers 03-24, Duke University, Department of Economics.
  • Handle: RePEc:duk:dukeec:03-24

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    References listed on IDEAS

    1. Jordi Gali, 1999. "Technology, Employment, and the Business Cycle: Do Technology Shocks Explain Aggregate Fluctuations?," American Economic Review, American Economic Association, vol. 89(1), pages 249-271, March.
    2. Elliott, Graham & Jansson, Michael, 2003. "Testing for unit roots with stationary covariates," Journal of Econometrics, Elsevier, vol. 115(1), pages 75-89, July.
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    10. Neville Francis & Valerie A. Ramey, 2002. "Is the Technology-Driven Real Business Cycle Hypothesis Dead?," NBER Working Papers 8726, National Bureau of Economic Research, Inc.
    11. Kilian, Lutz & Chang, Pao-Li, 2000. "How accurate are confidence intervals for impulse responses in large VAR models?," Economics Letters, Elsevier, vol. 69(3), pages 299-307, December.
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    Cited by:

    1. Norman Swanson & Nii Ayi Armah, 2006. "Predictive Inference Under Model Misspecification with an Application to Assessing the Marginal Predictive Content of Money for Output," Departmental Working Papers 200619, Rutgers University, Department of Economics.
    2. Clark, Todd & McCracken, Michael, 2013. "Advances in Forecast Evaluation," Handbook of Economic Forecasting, Elsevier.
    3. Todd E. Clark & Michael W. McCracken, 2009. "Improving Forecast Accuracy By Combining Recursive And Rolling Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 50(2), pages 363-395, May.
    4. Luca Fanelli & Giulio Palomba, 2011. "Simulation‐based tests of forward‐looking models under VAR learning dynamics," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(5), pages 762-782, August.
    5. Garratt, Anthony & Koop, Gary & Mise, Emi & Vahey, Shaun P., 2009. "Real-Time Prediction With U.K. Monetary Aggregates in the Presence of Model Uncertainty," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 480-491.
    6. Dean Croushore, 2011. "Frontiers of Real-Time Data Analysis," Journal of Economic Literature, American Economic Association, vol. 49(1), pages 72-100, March.
    7. Valentina Corradi & Norman R. Swanson, 2007. "Nonparametric Bootstrap Procedures For Predictive Inference Based On Recursive Estimation Schemes," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 48(1), pages 67-109, February.
    8. Anatolyev, Stanislav, 2009. "Nonparametric Retrospection and Monitoring of Predictability of Financial Returns," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(2), pages 149-160.
    9. Nicolau, Mihaela & Palomba, Giulio, 2015. "Dynamic relationships between spot and futures prices. The case of energy and gold commodities," Resources Policy, Elsevier, vol. 45(C), pages 130-143.
    10. Valentina Corradi & Norman Swanson, 2004. "Bootstrap Procedures for Recursive Estimation Schemes With Applications to Forecast Model Selection," Departmental Working Papers 200418, Rutgers University, Department of Economics.
    11. Mihaela NICOLAU & Giulio PALOMBA & Ilaria TRAINI, 2013. "Are Futures Prices Influenced by Spot;Prices or Vice-versa? An Analysis of Crude;Oil, Natural Gas and Gold Markets," Working Papers 394, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    12. Rossi, Barbara & Sekhposyan, Tatevik, 2010. "Have economic models' forecasting performance for US output growth and inflation changed over time, and when?," International Journal of Forecasting, Elsevier, vol. 26(4), pages 808-835, October.
    13. Fanelli, Luca, 2007. "Evaluating the New Keynesian Phillips Curve under VAR-based learning," MPRA Paper 1616, University Library of Munich, Germany.
    14. Fanelli, Luca, 2008. "Evaluating New Keynesian Phillips Curve under VAR-Based Learning," Economics - The Open-Access, Open-Assessment E-Journal, Kiel Institute for the World Economy (IfW), vol. 2, pages 1-24.
    15. Stanislav Anatolyev & Grigory Kosenok, 2011. "Sequential Testing with Uniformly Distributed Size," Working Papers w0123, Center for Economic and Financial Research (CEFIR).

    More about this item

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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