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Found 6 papers, 0 papers with code
Overidentification in Shift-Share Designs
We further show that homogeneous effect models in short panels, and their corresponding overidentification tests, are of central importance by establishing that: (i) In heterogenous effects models, interpreting TSLS as a positively weighted average of treatment effects can impose implausible assumptions on the distribution of the data; and (ii) Alternative identifying strategies relying on long panels can prove uninformative in short panel applications.
Stratifying on Treatment Status
In the case of unconfounded assignment where the potential outcomes are independent of the treatment given covariates, we show that standard estimators of the average treatment effect are inconsistent.
Some Finite-Sample Results on the Hausman Test
This paper shows that the endogeneity test using the control function approach in linear instrumental variable models is a variant of the Hausman test.
Logit-based alternatives to two-stage least squares
We propose logit-based IV and augmented logit-based IV estimators that serve as alternatives to the traditionally used 2SLS estimator in the model where both the endogenous treatment variable and the corresponding instrument are binary.
Standard errors when a regressor is randomly assigned
In particular, when the regressor of interest is independent not only of other regressors but also of the error term, the textbook homoskedastic variance formula is valid even if the error term and auxiliary regressors exhibit a general dependence structure.
Efficient Bias Correction for Cross-section and Panel Data
In particular, we find that for a variety of estimators the straightforward bootstrap bias correction gives the same higher-order variance as more complicated analytical or jackknife bias corrections.
