报告题目：Deep learning based doubly robust test for Granger causality

主讲人：宋晓军副教授（北京大学）

时间：2026年6月16日（周二）14:00 p.m.

地点：北院卓远楼305会议室

主办单位：统计与数学学院

摘要：

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 $n^{-1/2}$. 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.

主讲人简介：

宋晓军，北京大学光华管理学院商务统计与经济计量系副教授，博士生导师，西班牙马德里卡洛斯三世大学经济学博士。主要研究兴趣是理论计量经济学，包括非参数/半参数方法，假设检验和自助法，以及计量经济学的应用等。论文发表在Econometric Reviews, Econometric Theory, Journal of Applied Econometrics, Journal of Business & Economic Statistics, Journal of Econometrics和Management Science等国际期刊。主持和参加自然科学基金面上项目和重点项目等。获得北京大学优秀班主任、北京大学优秀博士学位论文指导教师、北京大学蔡元培美育奖教金等荣誉。自2020年1月起，担任Economic Modelling副主编。