This dissertation consists of three essays on the identification analysis of econometric models.
The first essay explores the identification question in semiparametric binary response models when all regressors have discrete support. I suggest a recursive procedure that finds sharp bounds on the parameter of interest and can be applied to...
The first chapter of this dissertation develops a two-stage inference method for structural parameters in the linear instrumental variables model. In the first stage, a new statistic is used to detect whether the correlation between the structural error and the reduced form error is small. In the second stage, a...
This thesis considers identification, estimation, and inference in nonparametric settings. Special attention is given to identification with instrumental variables of small support, and in time series via the ergodic assumption. Chapter 1 considers a nonparametric instrumental regression model in which the regressor and instrument are discretely distributed. Here, we strengthen...
This dissertation proposes an oracle efficient estimator in the context of a sparse linear model. Chapter 1 introduces the penalty and the estimator that optimizes a penalized least squares objective. Unlike existing methods, the penalty is differentiable – once, and hence the estimator does not engage in model selection. This...