Abstract Using a sample of firms over the 2002–2010 period relative to US, Europe, and Japan, this paper investigates effects control corruption on firms’ efficiency. Our econometric analysis is developed into two main steps. In first step, we rely application Stochastic Frontier Analysis (SFA) estimate firm-level We then regress derived efficiency scores against International Country Risk Guid...

This paper studies robustness of bootstrap inference methods for instrumental variable regression models. In particular, we compare the uniform weight and implied probability bootstrap approximations for parameter hypothesis test statistics by applying the breakdown point theory, which focuses on behaviors of the bootstrap quantiles when outliers take arbitrarily large values. The implied proba...

Many applications involve a censored dependent variable and an endogenous independent variable. Chernozhukov et al. (2015) introduced a censored quantile instrumental variable estimator (CQIV) for use in those applications, which has been applied by Kowalski (2016), among others. In this article, we introduce a Stata command, cqiv, that simplifes application of the CQIV estimator in Stata. We s...

Validity of an Instrumental Variable Fan Yang, José R. Zubizarreta, Dylan S. Small, Scott Lorch, Paul R. Rosenbaum1 University of Pennsylvania and Columbia University Abstract: An instrument or instrumental variable is often used in an effort to avoid selection bias in inference about the effects of treatments when treatment choice is based on thoughtful deliberation. An instrument is a haphaza...

The convergence properties of a recently developed recursive subspace identification algorithm are investigated in this paper. The algorithm operates on the basis of an extended instrumental variable (EIV) version of the propagator method for signal subspace estimation. It is proved that, under weak conditions on the input signal and the identified system, the considered MOESP class of recursiv...

Instrumental Variable (IV) estimation is a powerful strategy for estimating causal influence, even in the presence of confounding. Standard IV estimation requires that the relationships between variables is linear. Here we relax the linearity requirement by constructing a piecewise linear IV estimator. Simulation studies show that when the causal influence of X on Y is non-linear, the piecewise...