追蹤資料的分量迴歸分析之內生性問題

Abstract

This project develops a two-stage estimation of a panel data quantile regression model with endogenous explanatory variable. The regressors in the model include a lagged endogenous dependent variable and other explanatory variables, that are correlated withthe fixed effects. In the estimation, the control function approach is used, and a penalized quantile regression method for panel data is applied in the second stage. The Monte Carlo simulation shows that the proposed estimation effectively reduces the endogenous bias and performs better than other estimators in finite samples.