题目：Nonparametric Identification and Estimation of Generalized Additive Model

主讲人：上海财经大学 周亚虹教授

主持人：统计学院 常晋源教授

时间：2021年11月2日（周二）下午15:30-16:30

地点：腾讯会议，273 480 776

报告摘要：

This paper proposes a nonparametric approach to identify and estimate (with sieves) the generalized additive model with arbitrary grouping and discrete variable(s) when the link function is unknown. Our approach allowing arbitrary grouping provides the foundation to design a data-driven inference procedure which finds the best grouping specification among all possible groupings, and allowing discrete variables is mainly motivated by concerns from applied research. Our method can be applied to study the treatment effect when the covariates affect the potential outcomes in a generalized additive way. We effectively transform the generalized additive model with unknown link function into a problem which is much easier to estimate by sieve approach. Our estimator for link function is shown to converge at a rate of one covariate, and estimators for component functions within the link can attain nonparametric rates of their own covariates. By simulation, we show that such a method has good performance in finite samples.

主讲人简介：

周亚虹，上海财经大学经济学院教授、院长，国家级人才项目特聘教授、“新世纪优秀人才”、“上海领军人才”、上海财经大学“英贤学者”。长期致力于微观计量经济学、微观经济学研究，在Journal of Econometrics、Journal of Business & Economic Statistics、Econometric Theory等国际知名期刊和《经济研究》等国内权威期刊发表文章数十篇，主持多项国家自然科学基金项目，包括1项国家自然科学基金重点项目。其研究成果先后取得上海市第十一届哲学社会科学优秀成果奖三等奖（2012年）、高等学校科学研究优秀成果奖（人文社会科学）三等奖（2013年）、上海市第十三届哲学社会科学优秀成果奖一等奖（2016年）和第六次全国优秀财政理论研究成果奖（2017年）。