Under-Identification of Structural Models Based on Timing and Information Set Assumptions

Speaker：Su Yingjun（Associate Professor ,Jinan University Institute of Economics and Social Research）

Host：Hao Tongtong, assistant professor, Lingnan College

Time and Date：14:30-16:30, Jun. 10, 2022

Venue: Third floor, Lingnan hall 

Language: English

Abstract:

We revisit identification based on timing and information set assumptions in structural models, which have been used in the context of production functions, demand equations, and hedonic pricing models (e.g. Olley and Pakes (1996), Blundell and Bond (2000)). First, we demonstrate a general under-identification problem using these assumptions in a simple version of the Blundell-Bond dynamic panel model. In particular, the basic moment conditions can yield multiple discrete solutions: one at the persistence parameter in the main equation and another at the persistence parameter governing the regressor. We then consider a broader set of models, showing that the problem can persist more generally, but also disappears in some cases, e.g. when one makes stronger timing assumptions. We then propose possible solutions in the simple setting, in part based on specifying a model for the endogenous right-hand-side variables and enforcing an assumed sign restriction. We conclude by using lessons from our basic identification approach for the simple model to propose more general practical advice for empirical researchers using these techniques.

 Profile of the speaker:

Su Yingjun is currently an associate professor of the Institute of Economics and Social Research of Jinan University and deputy editor of China Economic Review. In 2017, he obtained a doctorate degree in economics from the University of Pittsburgh. The research field is industrial organizations and applied economics. The research results were published in the international authoritative economy journals such as Review of Economics and Statistics and Journal of Applied Economics to host the National Natural Science Foundation of China.