题目：Shrinkage Quantile Regression Estimation for Panel Data Models with Multiple Structural Breaks
In this paper, we consider a high-dimensional quantile regression model for panel data with multiple structural breaks. We develop a penalzied estimation method with both slope coefficients and individual fixed effects by combining the informations over multiple quantile levels. We show that with probability tending to one our proposed method can correctly determine the unknown number of the the breaks and estimate the common break dates consistently. The asymptotic distributions of the Lasso estimators of the regression coefficients and the post Lasso versions are also established.
Simulation results demostrate that the proposed method works well in the finite samples. The perfomance of the the proposed method is futher illustrated by the analysis of a environmental Kuznets curves data.