This paper studies estimation and specification testing in threshold regression with endogeneity. Three key results differ from those in regular models. First, both the threshold point and the threshold effect parameters are shown to be identified without the need for instrumentation. Second, in...

In this paper, we study partial identification of the distribution of treatment effects of a binary treatment for ideal randomized experiments, ideal randomized experiments with a known value of a dependence measure, and for data satisfying the selection-on-observables assumption respectively....

We propose the sharp identifiable bounds of the distribution functions of potential outcomes using a panel with fixed T. We allow for the possibility that the statistical randomization of treatment assignments is not achieved until unobserved heterogeneity is properly controlled for. We use...

This paper presents some selective aspects of standard econometric methods and of new developments in econometrics that are important for applications with microeconomic data. The range includes variance estimators, measurement of outliers, problems of partially identified parameters, nonlinear...

The moment conditions or estimating equations for instrumental variables quantile regression involves the discontinuous indicator function. We instead use smoothed estimating equations, with bandwidth h. This is known to allow higher-order expansions that justify bootstrap refinements for...

In this paper, we discuss the key conditions for the identification and estimation of the local average treatment effect (LATE, Imbens and Angrist, 1994): the valid instrument assumption (LI) and the monotonicity assumption (LM). We show that the joint assumptions of LI and LM have a testable...

We propose inverse probability weighted estimators for the local average treatment effect (LATE) and the local average treatment effect for the treated (LATT) under instrumental variable assumptions with covariates. We show that these estimators are asymptotically normal and efficient. When the...

We propose a kernel-based estimator for a partially linear model in triangular systems where endogenous variables appear both in the nonparametric and linear component functions. Our estimator is easy to implement, has an explicit algebraic structure, and exhibits good finite sample performance...

We establish the consistency and asymptotic normality for a class of estimators that are linear combinations of a set of v n- consistent estimators whose cardinality increases with sample size. A special case of our framework corresponds to the conditional moment restriction and the implied...

We consider endogenous attrition in panels where the probability of attrition may depend on current and past outcomes. We show that this probability is nonparametrically identified provided that instruments affecting the outcomes but not directly attrition, and whose distribution is identified,...