fixed effects demeaning in the presence of interactive effects in treatment effects regressions and elsewhere (replication data)
The present paper shows that cross-section demeaning with respect to time fixed effects is more useful than commonly appreciated, in that it enables consistent and asymptotically normal estimation of interactive effects models with heterogeneous slope coefficients when the number of time periods, T, is small and only the number of cross-sectional units, N, is large. This is important when using OLS but also when using more sophisticated estimators of interactive effects models whose validity does not require demeaning, a point that to the best of our knowledge has not been made before in the literature. As an illustration, we consider the problem of estimating the average treatment effect in the presence of unobserved time-varying heterogeneity. Gobillon and Magnac (2016) recently considered this problem. They employed a principal components-based approach designed to deal with general unobserved heterogeneity, which does not require fixed effects demeaning. The approach does, however, require that T is large, which is typically not the case in practice, and the results reported here confirm that the performance can be extremely poor in small-T samples. The exception is when the approach is applied to data that have been demeaned with respect to fixed effects.
Fixed effects demeaning in the presence of interactive effects in treatment effects regressions and elsewhere (replication data).
Journal of Applied Econometrics.
Petrova, Y. and Westerlund, J. (2020), Fixed Effects Demeaning In The Presence Of Interactive Effects In Treatment Effects Regressions And Elsewhere, Journal of Applied Econometrics, 35(7), 960-964. https://doi.org/10.1002/jae.2790