Panel Data Models With Interactive Fixed Effects and Multiple Structural Breaks

Degui Li, Junhui Qian and Liangjun Su ()

Journal of the American Statistical Association, 2016, vol. 111, issue 516, 1804-1819

Abstract: 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.

Date: 2016
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DOI: 10.1080/01621459.2015.1119696

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