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NEW ROBUST INFERENCE FOR PREDICTIVE REGRESSIONS

Published online by Cambridge University Press:  03 May 2023

Rustam Ibragimov ,
Jihyun Kim  and
Anton Skrobotov
Rustam Ibragimov
Affiliation:
Imperial College Business School and Center for Econometrics and Business Analytics, St. Petersburg University
Jihyun Kim*
Affiliation:
Sungkyunkwan University and Toulouse School of Economics
Anton Skrobotov
Affiliation:
Russian Presidential Academy of National Economy and Public Administration and St. Petersburg University
*
Address correspondence to Jihyun Kim, School of Economics, Sungkyunkwan University, Seoul 03063, South Korea; e-mail: [email protected].
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Abstract

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We propose a robust inference method for predictive regression models under heterogeneously persistent volatility as well as endogeneity, persistence, or heavy-tailedness of regressors. This approach relies on two methodologies, nonlinear instrumental variable estimation and volatility correction, which are used to deal with the aforementioned characteristics of regressors and volatility, respectively. Our method is simple to implement and is applicable both in the case of continuous and discrete time models. According to our simulation study, the proposed method performs well compared with widely used alternative inference procedures in terms of its finite sample properties in various dependence and persistence settings observed in real-world financial and economic markets.

Type
ARTICLES
Information
Econometric Theory , Volume 40 , Issue 6 , December 2024 , pp. 1364 - 1390
Copyright
© The Author(s), 2023. Published by Cambridge University Press

Footnotes

We thank the Editor (Peter C.B. Phillips), the Co-Editor (Anna Mikusheva), and three anonymous referees for many helpful comments and suggestions. We are also grateful to Walter Distaso, Jean-Marie Dufour, Siyun He, Nour Meddahi, Mikkel Plagborg-Møller, Aleksey Min, Ulrich K. Müller, Rasmus S. Pedersen, Artem Prokhorov, Rogier Quaedvlieg, Robert Taylor, Alex Maynard, Neil Shephard, Yang Zu, and the participants at the Center for Econometrics and Business Analytics (CEBA, St. Petersburg University) and University of Nottingham seminar series, the session on Econometrics of Time Series at the 12th World Congress of the Econometric Society, and iCEBA-2021, 2022 conferences for helpful discussions and comments. Financial support from a British Academy/Leverhulme Small Research Grant (R. Ibragimov, reference SG2122$\backslash$211040), the French Government and the ANR’ Investissements d’Avenir program (J. Kim, Grant ANR-17-EURE-0010) and the Russian Science Foundation (A. Skrobotov, Project No. 20-78-10113) for various and non-overlapping parts of this research is gratefully acknowledged.

References

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