Abstract:
This paper presents estimation of panel data regression models with individual
effects. We discuss estimation techniques for both fixed and random effects panel
data regression models. We derive two-stage least squares and generalized least
squares estimators, and discuss their limitations. Under specified conditions, we
investigate the asymptotic properties of the derived estimators, in particular, the
consistency and asymptotic normality, and the Hausman test for panel data
regression models with large number of cross-section and fixed time-series
observations. We show that both estimators are consistent and asymptotically
normally distributed and have different convergence rates dependent on the
assumptions of the regressors and the remainder disturbances. We also perform
simulation studies to see the performance of our estimates for large cross sections.
Our simulation results show that the estimator based on the bigger sample is more
consistent than the one based on the smaller sample size. We find that the twostage
least squares estimator performs better in the presence of endogeneity, while
the generalized least squares estimator performs better under strict exogeneity
conditions. We also note that the generalized least squares estimator performs
better than the ordinary least squares estimator in the absence of correlation
between individual effects and the regressors.
