An Alternative Bootstrap Procedure for Factor-Augmented Regression Models  

Prof. Takashi YAMAGATA
Professor of Economics
Department of Economics and Related Studies
University of York

Date: 7 January 2026 (Wednesday)
Time: 10:30-12:00
Venue: E22-G015
Host: Prof. Jia CHEN, Professor in Economics, FSS / Prof. Yi DING, Assistant Professor in Business Economics, FBA

Abstract

In this paper, we propose a novel bootstrap algorithm that is more efficient than existing methods for approximating the distribution of the factor-augmented regression estimator for a rotated parameter vector. The regression is augmented by r factors extracted from a large panel of N variables observed over T time periods. We consider general weak factor (WF) models with r signal eigenvalues that may diverge at different rates, Nαk , where 0 < αk ≤ 1 for k = 1, 2, …, r. We establish the asymptotic validity of our bootstrap method using not only the conventional data-dependent rotation matrix $\hat{\bH}$, but also an alternative data-dependent rotation matrix, $\hat{\bH}_q$, which typically exhibits smaller asymptotic bias and achieves a faster convergence rate. Furthermore, we demonstrate the asymptotic validity of the bootstrap under a purely signal-dependent rotation matrix $\hat{\bH}$, which is unique and can be regarded as the population analogue of both $\hat{\bH}$, and $\hat{\bH}_q$. Experimental results provide compelling evidence that the proposed bootstrap procedure achieves superior performance relative to the existing procedure.

Speaker

Prof. Takashi YAMAGATA is a Professor of Economics at the University of York, UK, and a Visiting Professor at Tohoku University. He specialises in economic statistics and data analysis. His research focuses on the development of statistical methods and their application in empirical analysis. His work is regularly presented at prestigious international conferences and invited seminars, and his papers are frequently published in leading journals such as the Journal of Econometrics, the Journal of the American Statistical Association, and the Journal of Business & Economic Statistics, among others.

All are welcome!