报告主题：Deep learning for semiparametric network vector autoregressive models

时间：2024年5月10日13:00-13:45

地点：上川路校区二教105/腾讯会议 会议号：517 4242 8784  密码：6666

报告人：唐一鸣

报告内容简介：

Network vector autoregressive models play a vital role in the multivariate time series analysis. However, previous research in the classic Network vector AutoRegressive (NAR) model is limited to strict assumptions of linearity and time-invariance of node-specific covariates. In this paper, we propose a Semiparametric NAR (SNAR) model to broaden the horizons by (1) extending node-specific covariates to a nonlinear framework, (2) incorporating high-dimensional time-varying covariates for a more comprehensive analysis, and (3) maintaining the interpretability of the autoregressive effects of NAR. A deep learning based method is presented to simultaneously estimate the nonparametric function and the parameters in SNAR. Meanwhile, we provide theoretical proof for the convergence rate of the nonparametric deep neural network estimator to support linear-to-nonlinear extension. It also shows that the proposed method is capable of avoiding the curse of dimensionality. Furthermore, we also prove the asymptotic normality of the parametric estimators for autoregressive effects to demonstrate the maintenance of interpretability. Experiments on various numerical simulated data and a real-world financial data set containing high-dimensional covariates demonstrate the effectiveness of the theoretical properties and the proposed method.

主讲人简介：

唐一鸣，上海立信会计金融学院讲师，常任轨教师，研究方向包括社交网络数据分析，纵向数据分析，非参数/半参数统计，机器学习。