The aim of this research is to investigate the relationship between obesity and wages, using data for nine countries from the European Community Household Panel (ECHP) over the period 1998-2001. We improve upon the existing literature by adopting an Instrumental Variable Quantile Regression...

In this paper, we consider sieve instrumental variable quantile regression (IVQR) estimation of functional coefficient models where the coefficients of endogenous regressors are unknown functions of some exogenous covariates. We approximate the unknown functional coefficients by some basis...

We study cluster-robust inference for binary response models. Inference based on the most commonly-used cluster-robust variance matrix estimator (CRVE) can be very unreliable. We study several alternatives. Conceptually the simplest of these, but also the most computationally demanding, involves...

Cluster-robust inference is widely used in modern empirical work in economics and many other disciplines. The key unit of observation is the cluster. We propose measures of "high-leverage" clusters and "influential" clusters for linear regression models. The measures of leverage and partial...

In this paper we design two split-sample score tests for subsets of structural coefficients in a linear Instrumental Variables (IV) regression. Sample splitting serves two purposes - 1) validity of the resultant tests does not depend on the identifiability of the coefficients being tested and 2)...

This paper shows how asymptotically valid inference in regression models based on the weighted least squares (WLS) estimator can be obtained even when the model for reweighting the data is misspecified. Like the ordinary least squares estimator, the WLS estimator can be accompanied by...

We consider inference based on local estimating equations in the presence of nuisance parameters. The framework is useful for a number of applications including those in economic policy evaluation based on discontinuities or kinks and in real-time financial risk management. We focus on the...

We propose a test for the key identification and estimation conditions in Regression Discontinuity (RD) designs. We characterize the set of sharp testable implications of the RD assumptions, for which the proposed test is uniformly valid under a class of distributions, is consistent against all...

In regression discontinuity design (RD), for a given bandwidth, researchers can estimate standard errors based on different variance formulas obtained under different asymptotic frameworks. In the traditional approach the bandwidth shrinks to zero as sample size increases; alternatively, the...

This paper proposes empirical likelihood based inference methods for causal effects identified from regression discontinuity designs. We consider both the sharp and fuzzy regression discontinuity designs and treat the regression functions as nonparametric. The proposed inference procedures do...