Yao Luo from Toronto will present  "Under-Identification of Structural Models Based on Timing and Information Set Assumptions" joint with Daniel Ackerberg, Kyoo il Kim, Yingjun Su, and Garth Frazer

Abstract:

We study identification based on timing and information set assumptions in many structural models, including production functions and dynamic panel approaches. First, we demonstrate a general under-identification problem in these models and illustrate it in dynamic panel approaches (e.g., Arellano and Bond (1991)). 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. Second, we propose possible solutions based on sign restrictions and an augmented moment approach. We show the identification of our approach and propose a consistent estimation procedure. Our Monte Carlo simulations illustrate the under-identification issue and finite sample performance of our proposed estimator. Lastly, we show that the problem persists in many alternative models of the regressor but disappears in some models under stronger assumptions.