报告题目： Clustering for Multi-Dimensional Heterogeneity
报告人：程旭 美国宾夕法尼亚大学
报告时间：2022年12月13日(周二) 10:00-11:30
Zoom会议 ID：893 7786 7510
内容摘要
This paper provides a new multi-dimensional clustering approach for unobserved heterogeneity in panel data models. Each unit is associated with multiple clusters. For example, a firm can belong to the high productivity group and the low output elasticity group. In contrast, the standard one-dimensional clustering approach would be based on separate groups for each productivity-elasticity pair. Our approach provides substantial gains in estimation accuracy when unobserved features have sparse interactions, e.g., there are only a few firms with high productivity and low output elasticity. We propose an estimator for the unobserved group memberships and the group-specific and common parameters in a nonlinear GMM framework and derive its large sample properties. In particular, we provide the first classification consistency result in a nonlinear GMM setup. We re-evaluate the rise of aggregate markup in De Loecker, Eeckhout, and Unger (2018) by replacing their sector-specific production functions with a cluster-based one. We find that the upward trajectory persists, but the magnitude is less pronounced after accounting for multi-dimensional heterogeneity.
主讲人简介
程旭，美国宾夕法尼亚大学经济学副教授，其研究涉及计量经济学领域的广泛议题，包括计量经济学模型的识别，基于似然函数和矩条件的估计和推断，以及压缩估计等。研究成果发表在Econometrica、Review of Economic Studies、Quantitative Economics、Journal of Econometrics、Econometric Theory、及Journal of Business & Economic Statistics等国际学术期刊上。目前担任Econometric Theory联合主编，Quantitative Economics、Econometrics Journal、Econometrics Review副主编，曾担任Journal of Econometrics、Econometric Theory、Journal of Business & Economic Statistics副主编。
