Estimation of linear dynamic panel data models with time‐invariant regressors
提出一种两步法估计动态Hausman-Taylor模型，先估计时变回归元系数，再用残差对时不变回归元回归，比同时估计更稳健，并推导了标准误调整公式，通过蒙特卡洛模拟和外商直接投资重力方程实例验证。
Summary We present a sequential approach to estimating a dynamic Hausman–Taylor model. We first estimate the coefficients of the time‐varying regressors and subsequently regress the first‐stage residuals on the time‐invariant regressors. In comparison to estimating all coefficients simultaneously, this two‐stage procedure is more robust against model misspecification, allows for a flexible choice of the first‐stage estimator, and enables simple testing of the overidentifying restrictions. For correct inference, we derive analytical standard error adjustments. We evaluate the finite‐sample properties with Monte Carlo simulations and apply the approach to a dynamic gravity equation for US outward foreign direct investment.