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Conditional Superior Predictive Ability

Author

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  • Jia Li
  • Zhipeng Liao
  • Rogier Quaedvlieg

Abstract

This article proposes a test for the conditional superior predictive ability (CSPA) of a family of forecasting methods with respect to a benchmark. The test is functional in nature: under the null hypothesis, the benchmark’s conditional expected loss is no more than those of the competitors, uniformly across all conditioning states. By inverting the CSPA tests for a set of benchmarks, we obtain confidence sets for the uniformly most superior method. The econometric inference pertains to testing conditional moment inequalities for time series data with general serial dependence, and we justify its asymptotic validity using a uniform non-parametric inference method based on a new strong approximation theory for mixingales. The usefulness of the method is demonstrated in empirical applications on volatility and inflation forecasting.

Suggested Citation

  • Jia Li & Zhipeng Liao & Rogier Quaedvlieg, 2022. "Conditional Superior Predictive Ability," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 89(2), pages 843-875.
    • Handle: RePEc:oup:restud:v:89:y:2022:i:2:p:843-875.
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    File URL: http://hdl.handle.net/10.1093/restud/rdab039
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    Citations

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    Cited by:

    1. Beck, Günter W. & Carstensen, Kai & Menz, Jan-Oliver & Schnorrenberger, Richard & Wieland, Elisabeth, 2023. "Nowcasting consumer price inflation using high-frequency scanner data: Evidence from Germany," Discussion Papers 34/2023, Deutsche Bundesbank.
    2. Oh, Dong Hwan & Patton, Andrew J., 2024. "Better the devil you know: Improved forecasts from imperfect models," Journal of Econometrics, Elsevier, vol. 242(1).
    3. Yang, Guo-Hui & Zhong, Guang-Yan & Wang, Li-Ya & Xie, Zu-Guang & Li, Jiang-Cheng, 2024. "A hybrid forecasting framework based on MCS and machine learning for higher dimensional and unbalanced systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 637(C).
    4. Romero, Eva, 2024. "Fitting complex stochastic volatility models using Laplace approximation," DES - Working Papers. Statistics and Econometrics. WS 43947, Universidad Carlos III de Madrid. Departamento de Estadística.
    5. repec:cte:wsrepe:36569 is not listed on IDEAS
    6. Corradi, Valentina & Fosten, Jack & Gutknecht, Daniel, 2023. "Out-of-sample tests for conditional quantile coverage an application to Growth-at-Risk," Journal of Econometrics, Elsevier, vol. 236(2).
    7. Nan Liu & Yanbo Liu & Yuya Sasaki, 2024. "Estimation and Inference for Causal Functions with Multiway Clustered Data," Papers 2409.06654, arXiv.org.
    8. David I. Harvey & Stephen J. Leybourne & Yang Zu, 2024. "Tests for equal forecast accuracy under heteroskedasticity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(5), pages 850-869, August.
    9. Yicun Li & Yuanyang Teng, 2023. "The Fama–French Five-Factor Model with Hurst Exponents Compared with Machine Learning Methods," Mathematics, MDPI, vol. 11(13), pages 1-19, July.
    10. Gorny, Paul M. & Groos, Eva & Strobel, Christina, 2024. "Do Personalized AI Predictions Change Subsequent Decision-Outcomes? The Impact of Human Oversight," MPRA Paper 121065, University Library of Munich, Germany.
    11. Zheng, Tingguo & Fan, Xinyue & Jin, Wei & Fang, Kuangnan, 2024. "Words or numbers? Macroeconomic nowcasting with textual and macroeconomic data," International Journal of Forecasting, Elsevier, vol. 40(2), pages 746-761.
    12. Romero, Eva, 2024. "A stochastic volatility model for volatility asymmetry and propagation," DES - Working Papers. Statistics and Econometrics. WS 43887, Universidad Carlos III de Madrid. Departamento de Estadística.

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