Deriving Some Estimators of Panel Data Regression Models with Individual Effects

The panel data models are becoming more common in relation to cross-section and time series models for innumerable present advantages, in addition to the computational advance that facilitated theirs utilization. The existing literature has focused on the application of the estimators. Many of panel data model estimators are severely inconsistent due to presence of endogeneity and heterogeneity problem. Hence, panel data offers various opportunities to derive estimators of that result consistent estimators. This paper considers estimation of linear panel data models with fixed effects and random effects when the equation of interest contains unobserved heterogeneity as well as endogenous explanatory variables. We offer a detailed analysis and derivation of the two-stage least squares (2SLS) and generalized least square (GLS) estimators in the context of panel data models.