597 citations · 1.3k across the 13 of their papers we have counts for
7 papers · 1 filter
When Can We Ignore Measurement Error in the Running Variable?
Yingying Dong, Michal Kolesár
In many applications of regression discontinuity designs, the running variable used by the administrator to assign treatment is only observed with error. We show that, provided the…
One Instrument to Rule Them All: The Bias and Coverage of Just-ID IV
Joshua Angrist, Michal Kolesár
We revisit the finite-sample behavior of single-variable just-identified instrumental variables (just-ID IV) estimators, arguing that in most microeconometric applications, the usu…
Contamination Bias in Linear Regressions
Paul Goldsmith-Pinkham, Peter Hull, Michal Kolesár
We study regressions with multiple treatments and a set of controls that is flexible enough to purge omitted variable bias. We show that these regressions generally fail to estimat…
Bias-Aware Inference in Regularized Regression Models
Timothy B. Armstrong, Michal Kolesár, Soonwoo Kwon
We consider inference on a scalar regression coefficient under a constraint on the magnitude of the control coefficients. A class of estimators based on a regularized propensity sc…
Robust Empirical Bayes Confidence Intervals
Timothy B. Armstrong, Michal Kolesár, Mikkel Plagborg-Møller
We construct robust empirical Bayes confidence intervals (EBCIs) in a normal means problem. The intervals are centered at the usual linear empirical Bayes estimator, but use a crit…
Sensitivity Analysis using Approximate Moment Condition Models
Timothy B. Armstrong, Michal Kolesár
We consider inference in models defined by approximate moment conditions. We show that near-optimal confidence intervals (CIs) can be formed by taking a generalized method of momen…
Shift-Share Designs: Theory and Inference
Rodrigo Adão, Michal Kolesár, Eduardo Morales
We study inference in shift-share regression designs, such as when a regional outcome is regressed on a weighted average of sectoral shocks, using regional sector shares as weights…