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Serial-Dependence and Persistence Robust Inference in Predictive Regressions

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  • Jean-Yves Pitarakis

Abstract

This paper introduces a new method for testing the statistical significance of estimated parameters in predictive regressions. The approach features a new family of test statistics that are robust to the degree of persistence of the predictors. Importantly, the method accounts for serial correlation and conditional heteroskedasticity without requiring any corrections or adjustments. This is achieved through a mechanism embedded within the test statistics that effectively decouples serial dependence present in the data. The limiting null distributions of these test statistics are shown to follow a chi-square distribution, and their asymptotic power under local alternatives is derived. A comprehensive set of simulation experiments illustrates their finite sample size and power properties.

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  • Jean-Yves Pitarakis, 2025. "Serial-Dependence and Persistence Robust Inference in Predictive Regressions," Papers 2502.00475, arXiv.org.
    • Handle: RePEc:arx:papers:2502.00475
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    1. Magdalinos, Tassos & Phillips, Peter C.B., 2009. "Limit Theory For Cointegrated Systems With Moderately Integrated And Moderately Explosive Regressors," Econometric Theory, Cambridge University Press, vol. 25(2), pages 482-526, April.
    2. Magdalinos, Tassos, 2022. "Least Squares And Ivx Limit Theory In Systems Of Predictive Regressions With Garch Innovations," Econometric Theory, Cambridge University Press, vol. 38(5), pages 875-912, October.
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