Since the late 90s, Regression Discontinuity (RD) designs have been widely used to estimate Local Average Treatment Effects (LATE). When the running variable is observed with continuous measurement error, identification fails. Assuming non-differential measurement error, we propose a consistent...

Understanding the relationship between disability and employment is critical and has long been the subject of study. However, estimating this relationship is difficult, particularly with survey data, since both disability and employment status are known to be misreported. Here, we use a partial...

In linear regression models, measurement error in a covariate causes Ordinary Least Squares (OLS) to be biased and inconsistent. Instrumental Variables (IV) is a common solution. While IV is also biased, it is consistent. Here, we undertake an asymptotic comparison of OLS and IV in the case...

We consider both a parametric and a semiparametric method to account for classification errors on the dependent variable in an ordered response model. The methods are applied to the analysis of self-reported speaking fluency of male immigrants in Germany. We find that a parametric model which...

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...

In empirical research, measuring correctly the benefits of welfare interventions is incredibly relevant for policymakers as well as academic researchers. Unfortunately, the endogenous program participation is often misreported in survey data and standard instrumental variable techniques are not...