Speaker：Dr. Yu Bai

　　Presentation Topic：Estimation and inference in large heterogeneous panels with stochastic time-varying coefficients 

　　Schedule：Nov 6, 2020 (Fri) 16:00-17:00

　　Location：腾讯会议 852 479 084

　　Abstract：In this paper, we develop kernel-based non-parametric estimation and inferential theory for large heterogeneous panel data models with stochastic time-varying coefficients. We propose mean group and pooled estimators, derive the asymptotic distributions, and show the uniform consistency and asymptotic normality of the coefficients paths of the model. Then, we extend the procedures to the case with possibly endogenous regressors and propose a time-varying version of the Hausman exogeneity test. The features of proposed estimators are investigated through an extensive Monte Carlo study. We also present two empirical applications. The first explores the time-varying price elasticity of U.S. gasoline demand functions with state-level data. The second estimates the panel version of time-varying backward looking and forward looking Phillips curve.  

题目：具有随机时变系数的大型异质面板中的估计和推断

摘要：在本文中，我们为具有随机时变系数的大型异构面板数据模型提出了基于核的非参数估计和推理理论。我们提出了平均组和合并估计量，推导出渐近分布，并显示模型系数路径的一致性和渐近正态性。然后，我们将程序扩展到可能具有内生变量的情况，并提出了 Hausman 外生性检验的时变版本。通过蒙特卡洛研究来寻找提议的估计器的特征。我们还提出了两个实证应用。第一个利用州级数据探讨了美国汽油需求函数的时变价格弹性。第二个估计面板版本的时变后视性和前瞻性菲利普斯曲线。