[* EiR = Econometrics in Replications, a feature of TRN that highlights useful econometrics procedures for re-analysing existing research.] NOTE: All the data and code necessary to produce the results in the tables below are available at Harvard’s Dataverse: click here. Fixed effects estimators are often used when researchers are concerned about omitted variable bias due to unobserved, time-invariant variables.
[* EiR = Econometrics in Replications, a feature of TRN that highlights useful econometrics procedures for re-analysing existing research. The material for this blog is primarily drawn from the recent working paper “ Difference-in-differences with variation in treatment timing” by Andrew Goodman-Bacon, available from his webpage at Vanderbilt University. FIGURE 1 is modified from a lecture slide by Pamela Jakiela and Owen Ozier.
[* EIR = Econometrics in Replications, a feature of TRN that highlights useful econometrics procedures for re-analysing existing research. The material for this blog is motivated by a recent blog at TRN, “ The problem isn’t just the p-value, it’s also the point-null hypothesis!” by Jae Kim and Andrew Robinson] In a recent blog, Jae Kim and Andrew Robinson highlight key points from their recent paper, “ Interval-Based Hypothesis Testing and Its Applications to Economics and Finance” (Econometrics, 2019).
[* EiR = Econometrics in Replications, a feature of TRN that highlights useful econometrics procedures for re-analysing existing research. The material for this blog is drawn from the recent working paper “ Two-way fixed effects estimators with heterogeneous treatment effects” by Clément de Chaisemartin and Xavier D’Haultfoeuille, posted at ArXiv.org] NOTE #1: All the data and code (Stata) necessary to produce the results in the tables below are available at Harvard’s Dataverse: click here.