Indirect Inference Estimation of Dynamic Panel Data Models
报告人： Yong Bao, Department of Economics, Purdue University, USA and Department of Economics, University of Macau, China
时间：2017-05-25 15:30 ~ 16:30
地点：Room 217, Guanghua Building 2
I propose a new estimator that inverts the approximate binding function using the asymptotic bias of the within-groups (WG) estimator in a first-order dynamic panel model with fixed effects under the asymptotic regime of large N and finite T. The new estimator is in the spirit of indirect inference (II) and corrects the inconsistency of the WG estimator. It is simulation-free, does not rely on any distributional assumption on the idiosyncratic error term, and is asymptotically normally distributed. Monte Carlo results indicate that the new estimator performs extremely well in finite samples and beats substantially other consistent estimators. It is also much computationally cheaper compared with the simulation-based estimator.
About the Speaker:
Dr. Yong Bao is an Associate Professor of Economics in the Krannert School of Management at Purdue University, USA. (Currently he is on leave from Purdue and is a Professor of Economics and Department Head at the University of Macau.) He received his BA in economics (with honor) from the University of International Business and Economics in 1998. During 1999-2004 he studied at the University of California, Riverside (UCR) and received his PhD in economics in 2004. His dissertation won the Graduate Research Award, the highest recognition of outstanding PhD dissertations at UCR. After graduation, Dr. Bao taught at the University of Texas at San Antonio (2004-2006) and Temple University (2007-2008) and joined Purdue University in 2008. His main research and teaching interests are in econometrics, empirical finance, and macroeconomics. Dr. Bao has published more than thirty research articles in leading economics journals. He has presented in many international conferences and given seminar talks at various institutions.