"This dissertation considers estimation and inference in three econometric models containing issues commonly encountered with observational data. Fundamental issues of self-selection, endogeneity, missing observations are pervasive in observational data. Moreover, often, the observations in a dataset are rarely statistically independent and have complex dependence structures. These issues can have a significant effect on the causal effect analysis and pose serious limitations on the popular methodologies that either maintain restrictive assumptions and/or require complicated and computationally tedious solutions.The dissertation aims to apply control function method as the primary tool to design estimation procedures under relaxed distributional and functional form assumptions. I describe computationally simple solutions to these issues to obtain more precise results. These estimation procedures are obtained under relaxed distributional and functional form assumptions allowing a researcher to incorporate more variability (or heterogeneity)."--from abstract.
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