The identification of causal effects in linear models relies, explicitly and implicitly, on the imposition of researcher beliefs along several dimensions. Assumptions about measurement error, regressor endogeneity, and instrument validity are three key components of any such empirical exercise....

The identification of causal effects in linear models relies, explicitly and implicitly, on the imposition of researcher beliefs along several dimensions. Assumptions about measurement error, regressor endogeneity, and instrument validity are three key components of any such empirical exercise....

Using board reforms in 41 countries and staggered difference-in-differences (DiD) estimates, Fauver, Hung, Li, and Taboada (2017, FHLT) find that firm value increases following the reforms. In a study that reviews the recent econometric theory on staggered DiD estimations, Baker, Larcker, and...

We illustrate the sensitivity of two-way fixed effects difference-indifferences estimates to innocuous changes in data structure. Using the staggered rollout of state-level bank branching deregulations, three outcome variables are brought to bear on the interventions: personal income growth (a...

We theoretically compare variances between the Infinitesimal Perturbation Analysis (IPA) estimator and the Likelihood Ratio (LR) estimator to Monte Carlo gradient for stochastic systems. The conditions proposed in [Cui et al., 2020] when the IPA estimator has a smaller variance can yield sharper...

This note formalizes the synthetic difference-in-differences estimator for staggered treatment adoption settings, as briefly described in Arkhangelsky et al. (2021). To illustrate the importance of this estimator, I use replication data from Abrams (2012), I compare the estimators obtained using...

This paper presents an inferential approach for the influential-imitator dynamics, a broadly accepted extension of the traditional Bass model, for cases in which the population has two segments: influentials (who influence each other) and imitators (whose choices are affected by the ones of...

Textbook theory predicts that t-ratios decline towards zero in regressions when there is collinearity between two regressors. This paper shows that this often does not occur if the regression suffers from simultaneity or omitted variable bias. With more collinearity, t-ratios generally increase...

In this article we study the widely used in randomized experiments approach of decreasing variance of estimate of average treatment effect (ATE) by estimating ATE on residuals using linear regression, i.e. when we first fit using linear regression on a pooled dataset exploratory...

The literature on heteroskedasticity and autocorrelation robust (HAR) inference is extensive but its usefulness relies on stationarity of the relevant process, say Vt, usually a function of the data and estimated model residuals. Yet, a large body of work shows widespread evidence of various...