I had a few hours of free time this weekend, which I used to read a new working paper by Abadie et al. (2017) titled “When Should You Adjust Standard Errors for Clustering?” I’m a little bit late to the party–David McKenzie blogged about this almost a full month ago–but doing all of my teaching in one semester leaves me considerably behind on reading new papers, which then leaves me behind on blogging.
This is a very nice paper, and it has seriously changed my understanding of clustering. Abadie et al. start with two common misconceptions regarding clustering:
- Clustering matters only if the residuals and the regressors are both correlated within clusters, and
- If clustering makes a difference in your standard errors, you should cluster.
A few weeks ago I was in a meeting with a team of graduate students with whom I am working on a research project. As we were going over their estimation results, I asked a few questions to make sure that those results were sound.
At some point, I asked: “Are there any generated regressors in those regressions?” Hearing no answer, I looked up and saw a bunch of puzzled faces looking back at me. Before I even began explaining, one of the students alluded to how this would make a good blog post.
Suppose you want to estimate the equation Continue reading
My friend and erstwhile colleague Tim Beatty (UC Davis), who currently serves as editor of the American Journal of Agricultural Economics, and his frequent coauthor Jay Shimshack (UVA), who has served as editor of the Journal of Environmental Economics and Management, have put together an extremely useful set of slides titled “Practical Tips for Writing and Publishing Applied Economics Papers” for a course they have been teaching. You can find those slides here.