In instrumental variables estimation with many instruments, like judge fixed effects designs, the precision of the jackknife-estimated first stage can vary widely across observations. When such variability exists, we show that the precision of JIVE second-stage estimates is meaningfully improved by shrinking judge propensities towards their conditional means, where the shrinkage factor depends on the precision of the first-stage fitted value. Doing so requires no further assumptions and identifies the same local average treatment effect as the usual (unshrunken) JIVE estimator. We illustrate our application in the context of the pre-trial detention of defendants.
The presence of multiple parameters complicates statistical inference. Ignoring the presence of multiple parameters can result in misleading inferences, while existing remedies often produce inferences too imprecise to be economically meaningful. This article proposes an approach to inference on multiple point- or partially identifed parameters that produces valid simultaneous inferences on multiple parameters while maintaining precision on the parameter or parameters of greatest interest. The approach allows inference to respect differing preferences for precision that the researcher may have across parameters, resulting in hypothesis tests that are more powerful and confidence regions with shorter projections on the parameters that the researcher cares more about, while remaining jointly valid across all parameters. A researcher using the procedure specifies in advance non-negative weights that correspond to the relative preference for precision across parameters. The proposed procedure chooses a confidence region to minimize the weighted sum of the projections on the parameter dimensions. A decision theoretic framework presents axioms for researcher preferences under which the proposed procedure is optimal. An empirical example from a field experiment on charitable giving shows the method offers substantial improvements in real-world settings.
This paper proposes a nonparametric estimator for treatment effects on censored outcomes when the treatment may be endogenous and have arbitrarily heterogeneous effects within a local average treatment effects framework. The proposed quantile treatment effects estimator is based on Kaplan-Meier estimators of the latent outcome cdf, and, relative to existing estimators, is efficient and robust to departures from the identifying assumptions. The paper derives the estimator's asymptotic distribution, illustrates its performance using Monte Carlo simulations, and applies it to a real world example.
Implements exact, finite-sample instrumental variables estimation and inference for a binary endogenous variable and binary instrument. Exact instrumental variables. Software package available for download under "Software" tab.