For those interested in some statistical self-flagellation, here’s a link to work in progress on estimation and inference with dyadic data, joint with Peter Aronow and Valentina Assenova: link. Dyadic data are ubiquitous in various fields of social science, including network sociology, international relations, and even research on “speed dating.” The problem of dyadic dependence complicates inference for such data. From what we’ve seen, most people either make hopeful assumptions about the nature of this dependence or just sweep it under the rug entirely. What we’ve done is to derive some results under highly “agnostic” assumptions, to show that on the one hand, the heavy parameterizations used in current approaches may be unnecessary, while on the other hand, ignoring dyadic dependence can be extremely misleading. We’re working on more applications and efficient software implementation. Comments appreciated.
Good stuff, Cyrus. I’m glad you guys cited Fafchamps and Gubert (2007), as this was one of the things I dropped by to mention (I’m also teaching it next week in the graduate development micro course I co-teach). In case you haven’t seen it, Ackerberg and Botticini (2002) is also a very influential paper on “endogenous matching.”