Under treatment effect heterogeneity, an instrument identifies the instrument-specific local average treatment effect (LATE). If a regression model is estimated by the two-stage least squares (2SLS) using multiple instruments, then 2SLS is consistent for a weighted average of different LATEs. In...

This paper shows how a weighted average of a forward and reverse Jackknife IV estimator (JIVE) yields estimators that are robust against heteroscedasticity and many instruments. These estimators, called HFUL (Heteroscedasticity robust Fuller) and HLIM (Heteroskedasticity robust limited...

Since identification, instrumental variables and variables exclusion, core concepts in econometrics, are entwined, several questions arise: How is identification related to the existence of IVs? How are identification criteria related to omitted variables? Is omission/inclusion of variables from...

We analyze the short- and long-term effects of the U.S. Vietnam-era military service on veterans' health outcomes using a restricted version of the National Health Interview Survey 1974-2013 and employing the draft lotteries as an instrumental variable (IV). We start by assessing whether the...

Individual outcomes are highly correlated with group average outcomes, a fact often interpreted as a causal peer effect. Without covariates, however, outcome-on-outcome peer effects are vacuous, either unity or, if the average is defined as a leave-out mean, determined by a generic intraclass...

We present a methodology for estimating the distributional effects of an endogenous treatment that varies at the group level when there are group-level unobservables, a quantile extension of Hausman and Taylor (1981). Standard quantile regression techniques are inconsistent in this setting, even...