经济与社会研究院SEMINAR第165期

题目：Inference in Partially Identified Panel Data Models with Interactive Fixed Effects

主讲人：洪圣杰, 清华大学

时间：2019年6月14日，13:30-15:00

地点：暨南大学中惠楼106B室

主讲人简介：

洪圣杰，清华大学经济与管理学院助理教授，美国威斯康辛麦迪逊分校博士。主要研究领域为非参数模型的统计推断，结构模型的估计识别，应用微观计量与中国经济。目前已经在Journal of Econometrics, 《管理世界》上发表系列文章。

Abstract:

This paper develops methods for statistical inferences in a partially identified nonparametric panel data model with endogeneity and interactive fixed effects. We consider the case where the number of cross-sectional units (N) is large and the number of time series periods (T) as well as the number of unobserved common factors (R) are fixed. Under some normalization rules, we can concentrate out the large dimensional parameter vector of factor loadings and specify a set of conditional moment restrictions that are involved with only the finite dimensional factor parameters along with the infinite dimensional nonparametric component. For a conjectured restriction on the parameter, we consider testing the null hypothesis that the restriction is satisfied by at least one element in the identified set and propose a test statistic based on a novel martingale difference divergence (MDD) measure for the distance between a conditional expectation object and zero. We derive the limiting distribution of the resultant test statistic under the null and show that it is divergent at rate-N under the global alternative based on the U-process theory. To obtain the critical values for our test, we propose a version of multiplier bootstrap and establish its asymptotic validity. Simulations demonstrate the finite sample properties of our inference procedure. We apply our method to study Engel curves for major nondurable expenditures in China by using a panel dataset from the China Family Panel Studies (CFPS).