In this article, we consider estimation of common structural breaks in panel data models with unobservable interactive fixed effects. We introduce a penalized principal component (PPC) estimation procedure with an adaptive group fused LASSO to detect the multiple structural breaks in the models. Under some mild conditions, we show that with probability approaching one the proposed 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. 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. Supplementary materials for this article are available online.
Change point, Interactive fixed effects, LASSO, Panel data, Penalized estimation, Principal component analysis
Journal of the American Statistical Association
Taylor & Francis: SSH Journals
LI, Degui; QIAN, Junhui; and SU, Liangjun.
Panel data models with interactive fixed effects and multiple structural breaks. (2016). Journal of the American Statistical Association. 111, (516), 1804-1819. Research Collection School Of Economics.
Available at: http://ink.library.smu.edu.sg/soe_research/1962
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.