太阳集团官网学术报告[2024] 003号

(高水平大学建设系列报告883号)

报告题目: Consistency, distributional convergence, and optimality of score-driven filters

报告人：林逸聪 助理教授（阿姆斯特丹自由大学）

报告时间：2024年01月17日下午15:30-16:30

报告地点： 汇星楼514

报告内容：We study the in-fill asymptotics of score-driven time series models. For general forms of model mis-specification, we show that score-driven filters are consistent for the Kullback-Leibler optimal time-varying parameter path, implying that for a correctly specified predictive conditional density, score-driven filters consistently estimate the true time-varying parameter path even if the model is dynamically mis-specified otherwise. We also obtain distributional convergence results for the filtering errors and derive the filter that minimizes the asymptotic filter error variance. Score-driven filters turn out to be optimal under correct specification of the predictive conditional density. The results considerably generalize earlier findings on the continuous-time consistency of volatility filters under mis-specification: they apply to biased filters, use weaker assumptions, accommodate more general forms of mis-specification, and consider general time-varying parameters in non-linear time series models beyond the volatility case. Illustrative examples discussed include time-varying tail shape models, dynamic copulas, and time-varying regression models.

报告人简介：林逸聪2013年本科毕业于深圳大学，2021年博士毕业于荷兰马斯特里赫特大学并获计量经济学博士学位。现于荷兰阿姆斯特丹自由大学担任助理教授及丁伯根研究所候选研究员。研究方向包括非线性，非平稳模型中的估计与推断方法以及信息论在机器学习中的应用。文章发表于Journal of Econometrics，Econometric Reviews。多次担任包括Journal of the American Statistical Association在内等多个杂志审稿人。

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报告邀请人：胡宗良

太阳集团官网

2024年01月11日