Panel Data Models with Interactive Fixed Effects and Multiple Structural

Speaker(s): 
Degui Li (York)
Date: 
Wednesday, October 29, 2014 - 10:00am
Location: 
WIAS, Erhard-Schmidt-Saal, Mohrenstraße 39, 10117 Berlin

In this paper we consider estimation and inference of common structural breaks in panel data models with interactive fixed effects which are unobservable. We introduce a penalized principal component estimation procedure via adaptive group fused LASSO to detect the multiple structural breaks. Under mild conditions, we show that with probability tending to one our method can correctly determine the unknown number of breaks and consistently estimate the common break dates. Furthermore, to improve the convergence rates, we estimate the regression coefficients through the post-LASSO method and establish the asymptotic distribution theory of the resulting estimators. We also propose a data-driven method to determine the tuning parameter involved in the penalized principal component estimation procedure. The Monte Carlo simulation results demonstrate that the proposed method works well in finite sample case.