Residual-based IV estimation of dynamic panel data models with fixed effects



In dynamic panel regression, when the variance ratio of individual effects to disturbance is large, the system-GMM estimator will have large asymptotic variance and poor finite sample performance. To deal with this variance ratio problem, we propose a residual-based instrumental variables (RIV) estimator, which uses the residual from regressing Δyi,t−1 on inline image as the instrument for the level equation. The RIV estimator proposed is consistent and asymptotically normal under general assumptions. More importantly, its asymptotic variance is almost unaffected by the variance ratio of individual effects to disturbance. Monte Carlo simulations show that the RIV estimator has better finite sample performance compared to alternative estimators. The RIV estimator generates less finite sample bias than difference-GMM, system-GMM, collapsing-GMM and Level-IV estimators in most cases. Under RIV estimation, the variance ratio problem is well controlled, and the empirical distribution of its t-statistic is similar to the standard normal distribution for moderate sample sizes.