On Friday, I gave a presentation on Morgan Kelly’s “The standard errors of persistence” a summary of which is available here.
The jist of Kelly’s work is that the persistence literature features unusually high t-statistics in part because of severe spatial autocorrelation in the residuals. When one accounts for this issue, he finds that the main persistence variable frequently has lower explanatory power than spatial noise. Furthermore, that persistence variable often strongly predicts spatial noise.
1. Introduction Recently, a friend asked me how to fit a two-way fixed effects model in R. A fixed effects model is a regression model in which the intercept of the model is allowed to move across individuals and groups. We most often see it in panel data contexts. Two-way fixed effects have seen massive interest from the methodological community. Some recent papers of interest are Imai and Kim 2019, Goodman-Bacon 2019, and Abraham and Sun 2018.
A common programming assignment when learning regression is to calculate OLS estimators by hand. In this post, I show exactly how to program OLS estimation in R. In addition, I explain how to add different standard error calculations to replicate Huber-White standard errors and Stata robust standard errors.