发布时间：2014-12-16 来源： [打印] 字号： T T T
Speaker：Degui Li (York University)
Time: 1:30pm, Dec.16th, 2014
Place: ISEM Conference Room (3rd floor, Chengming Building)
Panel Data Models with Interactive Fixed Effects and Multiple Structural Breaks
Abstract: In this paper we consider estimation of common structural breaks in panel data models with interactive fixed effects which are unobservable. We introduce a penalized principal component (PPC) estimation procedure with adaptive group fused LASSO to detect the multiple structural breaks in the models. Under some mild conditions, we show that with probability approaching one our method can correctly determine the unknown number of breaks and consistently estimate the common break dates. Furthermore, we estimate the regression coefficients through the post-LASSO method and establish the asymptotic distribution theory for the resulting estimators. The developed methodology and theory are applicable to the case of dynamic panel data models, and a data-driven method is proposed to determine the tuning parameter involved in the PPC estimation procedure. The Monte Carlo simulation results demonstrate that the proposed method works well in finite samples with low false detection probability when there is no structural break and high probability of correctly estimating the break numbers when the structural breaks exist. We finally apply our method to study the environmental Kuznets curve for 74 countries over 40 years and detect two breaks in the data.