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Recursive Predictability Tests for Real-Time Data

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Author Info
Rossi, Barbara
Inoue, Atsushi

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Abstract

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.

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Paper provided by Duke University, Department of Economics in its series Working Papers with number 03-24.

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Date of creation: 2003
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Handle: RePEc:duk:dukeec:03-24

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Find related papers by JEL classification:
C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation and Testing
C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications

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  1. Fanelli, Luca, 2008. "Evaluating the New Keynesian Phillips Curve under VAR-Based Learning," Economics Discussion Papers 2008-15, Kiel Institute for the World Economy. [Downloadable!]
    Other versions:
  2. Norman Swanson & Valentina Corradi, 2006. "Nonparametric Bootstrap Procedures for Predictive Inference Based on Recursive Estimation Schemes," Departmental Working Papers 200618, Rutgers University, Department of Economics. [Downloadable!]
    Other versions:
  3. 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, vol. 2(33), pages 1-24. [Downloadable!]
  4. 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. [Downloadable!]
  5. Todd E. Clark & Michael W. McCracken, 2004. "Improving forecast accuracy by combining recursive and rolling forecasts," Research Working Paper RWP 04-10, Federal Reserve Bank of Kansas City. [Downloadable!]
    Other versions:
  6. Stanislav Anatolyev, 2006. "Nonparametric retrospection and monitoring of predictability of financial returns," Working Papers w0071, Center for Economic and Financial Research (CEFIR). [Downloadable!]
    Other versions:
  7. Tatevik Sekhposyan & Barbara Rossi, 2008. "Has models’ forecasting performance for US output growth and inflation changed over time, and when?," Working Papers 09-02, Duke University, Department of Economics. [Downloadable!]
  8. Anthony Garratt & Gary Koop & Emi Mise & Shaun Vahey, 2008. "Real-time Prediction with UK Monetary Aggregates in the Presence of Model Uncertainty," Reserve Bank of New Zealand Discussion Paper Series DP2008/13, Reserve Bank of New Zealand. [Downloadable!]
    Other versions:
  9. Stanislav Anatolyev & Grigory Kosenok, 2008. "Sequential Testing with Uniformly Distributed Size," Working Papers w0123, Center for Economic and Financial Research (CEFIR). [Downloadable!]
  10. Tatevik Sekhposyan & Barbara Rossi, 2009. "Has Economic Models’ Forecasting Performance for US Output Growth and Inflation Changed Over Time, and When?," Working Papers 09-06, Duke University, Department of Economics. [Downloadable!]
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