Deep Learning Based Doubly Robust Test for Granger Causality

Speaker：Song Xiaojun, Associate Professor, Peking University

Host：Liu Xiaobin, Associate Professor, Lingnan College

Time and Date：15:00, June 24, 2026 (Wednesday)

Venue：Wang Dao Han Conference Room (101), Lingnan Hall

Language：Chinese

Abstract：

Granger causality is a fundamental concept for analyzing dynamic relationships in time series data, with widespread applications across the natural and social sciences, including genomics, neuroscience, economics, and finance. Consequently, nonparametric Granger causality testing has remained a central focus in econometrics for decades. Leveraging recent theoretical breakthroughs in deep learning, we propose a novel deep learning-based doubly robust Granger causality test (DRGCT). Our methodology offers several compelling advantages. First, for empirical practitioners, DRGCT naturally accommodates large lag orders, effectively circumventing the curse of dimensionality that inherently cripples traditional smoothing-based nonparametric tests. Second, from a theoretical perspective, our doubly robust moment construction elegantly neutralizes the slow convergence rates of deep neural networks. This allows the test statistic to achieve a parametric convergence rate, thereby establishing a new paradigm for valid nonparametric inference using deep learning in econometrics. Third, we develop a computationally efficient multiplier bootstrap procedure that flawlessly replicates the complex temporal covariance structure without redundant network retraining, further robustifying our test against general non-Markovian dynamics via a block-based extension. Theoretically, we prove that our test asymptotically controls the type I error, achieves an asymptotic power of one against fixed alternatives, and possesses non-trivial local power against alternatives converging at the optimal parametric rate n1/2. Finally, we validate the finite-sample performance of DRGCT through extensive numerical simulations and apply it to revisit the intricate price-volume relationships in the stock markets of the United States, China, and Japan.

Profile:

Song Xiaojun is an Associate Professor in the Department of Business Statistics and Econometrics at Guanghua School of Management, Peking University, with a Ph.D. in Economics from Universidad Carlos III de Madrid. doctoral supervisorHis primary research interests include theoretical econometrics, encompassing nonparametric/semiparametric methods, hypothesis testing, bootstrap methods, and applied econometrics. His papers have been published in international journals such as Management Science, Econometric Reviews, Econometric Theory, Journal of Applied Econometrics, Journal of Business & Economic Statistics, Journal of Econometrics, and Management Science. He has led and participated in National Natural Science Foundation projects, including general and key programs. Awards include the Peking University Outstanding Class Advisor, Peking University Outstanding Doctoral Thesis Supervisor, and the Peking University Cai Yuanpei Award for Aesthetic Education. Since January 2020, he has served as Deputy Editor-in-Chief of Economic Modelling.