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计量、金融与大数据分析workshop:Common Correlated Effects Estimation of Nonlinear Panel Data Models
发布日期：2024-05-10 12:00 来源：
题目：Common Correlated Effects Estimation of Nonlinear Panel Data Models
摘要：This paper focuses on estimating the coefficients and average partial effects of observed regressors in nonlinear panel data models with interactive fixed effects, using the common correlated effects (CCE) framework. The proposed two-step estimation method involves applying principal component analysis to estimate latent factors based on cross-sectional averages of the regressors in the first step, and jointly estimating the coefficients of the regressors and factor loadings in the second step. The asymptotic distributions of the proposed estimators are derived under general conditions, assuming that the number of time-series observations is comparable to the number of cross-sectional observations. To correct for asymptotic biases of the estimators, we introduce both analytical and split-panel jackknife methods, and confirm their good performance in finite samples using Monte Carlo simulations. An empirical application utilizes the proposed method to study the arbitrage behaviour of nonfinancial firms across different security markets.
主讲人：Liang Chen, HSBC Business School, Peking University
主持人：（国发院）黄卓、张俊妮、孙振庭
（北大新结构经济学研究院）胡博
（经济学院）王一鸣、王熙、刘蕴霆、王法
时间：2024年5月10日（周五）10:00 AM -- 11:30 AM
地点：北京大学国家发展研究院（承泽园）345会议室
主讲人简介：
Liang Chen is an Assistant Professor at HSBC Business School at Peking University and received his PhD in Economics from Universidad Carlos III de Madrid. Liang Chen' s main research interests include Econometric theory, applied econometrics. He has published papers in Econometrica, Economica, Journal of Econometrics and The Econometrics Journal.
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首页
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计量、金融与大数据分析workshop:Common Correlated Effects Estimation of Nonlinear Panel Data Models
发布日期：2024-05-10 12:00 来源：
题目：Common Correlated Effects Estimation of Nonlinear Panel Data Models
摘要：This paper focuses on estimating the coefficients and average partial effects of observed regressors in nonlinear panel data models with interactive fixed effects, using the common correlated effects (CCE) framework. The proposed two-step estimation method involves applying principal component analysis to estimate latent factors based on cross-sectional averages of the regressors in the first step, and jointly estimating the coefficients of the regressors and factor loadings in the second step. The asymptotic distributions of the proposed estimators are derived under general conditions, assuming that the number of time-series observations is comparable to the number of cross-sectional observations. To correct for asymptotic biases of the estimators, we introduce both analytical and split-panel jackknife methods, and confirm their good performance in finite samples using Monte Carlo simulations. An empirical application utilizes the proposed method to study the arbitrage behaviour of nonfinancial firms across different security markets.
主讲人：Liang Chen, HSBC Business School, Peking University
主持人：（国发院）黄卓、张俊妮、孙振庭
（北大新结构经济学研究院）胡博
（经济学院）王一鸣、王熙、刘蕴霆、王法
时间：2024年5月10日（周五）10:00 AM -- 11:30 AM
地点：北京大学国家发展研究院（承泽园）345会议室
主讲人简介：
Liang Chen is an Assistant Professor at HSBC Business School at Peking University and received his PhD in Economics from Universidad Carlos III de Madrid. Liang Chen' s main research interests include Econometric theory, applied econometrics. He has published papers in Econometrica, Economica, Journal of Econometrics and The Econometrics Journal.
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