Nickell Meets Stambaugh: A Tale of Two Biases in Panel Predictive Regressions
Speaker：Mei Ziwei, Assistant Professor, University of Macau
Host：Zhang Yifan, Associate Professor, Lingnan College
Time and Date：14:30, April 21, 2026 (Tuesday)
Venue：W.T.Chan Auditorium, Lingnan Hall
Language：Chinese
Abstract：
In panel predictive regressions with persistent covariates, coexistence of the Nickell bias and the Stambaugh bias imposes challenges for hypothesis testing. This paper introduces a new estimator, the Double IVX, which effectively removes this composite bias and reinstates standard inferential procedures. The Double IVX estimator is inspired by the IVX technique in time series. In panel data where the cross section is of the same order as the time dimension, the bias of the baseline panel IVX estimator can be corrected via an analytical formula by leveraging a tailored IVX estimator for a panel AR model. Double IVX is the first procedure that achieves unified inference across a wide range of modes of persistence in panel predictive regressions when the number of cross sections and the time span are comparably large. Such unified inference is unattainable for existing methods, including the popular within-group estimator. We apply Double IVX to panel data of financial markets in developed economies to examine the predictability of stock returns, where the results highlight the necessity of bias correction.
Profile:
Mei Ziwei is an Assistant Professor in Business Economics at Faculty of Business Administration, University of Macau. He obtained his Ph.D. degree in Economics in 2025 from the Chinese University of Hong Kong, and his bachelor's degree in 2020 from Lingnan College, Sun Yat-sen University. He specializes in econometric theory, with research interests covering high dimensional data analysis, nonstationary time series, dynamic panel models, and instrumental variable regressions. His research work has appeared in international journals, including Journal of Econometrics, Journal of International Economics, Journal of Business & Economic Statistics, etc.
