Essays in panel data econometrics examining selection bias and average treatment effects

Chapter 1 considers the affect of time varying unobserved effects in an unbalanced panel data model with possible selectivity bias. As in previous work dealing with sample selection, the unobserved effects in the regression and selection equations are allowed to be correlated with the regressors. Prior to testing for selectivity bias, the parameters of interest are estimated using Generalized Method of Moments. A minimum distance procedure is used to correct for selection bias. An empirical application dealing with a wage equation is used to illustrate the testing and correction procedures outlined in this chapter.; Keywords. Panel Data; Sample Selection; Time Varying Unobserved Effects; Conditional Mean Independence; Generalized Method of Moments; Minimum Distance Estimation; In Chapter 2, we consider nonlinear panel data models with possible sample selection bias. Previous work has shown the robustness properties of the quasi-maximum likelihood estimator under a conditional mean assumption. One can exploit the robustness properties of this estimator to test and correct for selection bias. Under a conditional mean assumption, a Generalized Method of Moments procedure is also available as an estimation method under suitable orthogonality conditions. An empirical example is used to illustrate the theory discussed in this chapter.; Keywords. Panel Data; Sample Selection; Robustness; Quasi-conditional maximum likelihood; Conditional Mean Independence; Generalized Method of Moments; Chapter 3 considers estimation of Average Treatment Effects (ATEs) for panel data models. Previous work has estimated the endogenous ATE with a correction function for cross-sectional data. The correlated random coefficient model gives us a framework from which to estimate ATEs, especially when the treatment variable is possibly endogenous. To account for endogeneity, we use a correction function estimator, which adds a function to correct for endogeneity bias. Monte Carlo simulations show that the correction function estimator performs well in finite samples. An empirical example illustrates the theory presented in this chapter by estimating the effect of the school choice program in Michigan on fourth grade student performance in mathematics.; Keywords. Panel Data; Average Treatment Effect; Correction Function; FE-IV; Correlated Random Coefficient; Endogeneity Bias...