Semiparametric-efficient estimation of AR(1) panel data models

This study focuses on the semiparametric-efficient estimation of random effect panel models containing AR(1) disturbances. We also consider such estimators when the effects and regressors are correlated (Hausman and Taylor, 1981). We introduce two semiparametric-efficient estimators that make minimal assumptions on the distribution of the random errors, effects, and the regressors and that provide semiparametric-efficient estimates of the slope parameters and of the effects. Our estimators extend the previous work of Park and Simar (J. Amer. Statist. Assoc. 89 (1994) 929), Park et al. (J. Econometrics 84 (1998) 273), and Adams et al. (J. Business Econom. Statist. 17 (1999) 349). Theoretical derivations are supplemented by Monte Carlo simulations. We also provide an empirical illustration by estimating relative efficiencies from a stochastic distance function for the U.S. banking industry over the 1980s and 1990s. In markets where regulatory constraints have been lessened or done away with, the deregulatory dynamic market shocks may not be adjusted to immediately and may induce a serial correlation pattern in firmʹs use of best-practice banking technologies. Our semiparametric estimators have an important role in providing robust point estimates and inferences of the productivity and efficiency gains due to such economic reforms.