Tom Clark writes:
Drew Linzer and I [Tom] have been working on a paper about the use of modeled (“random”) and unmodeled (“fixed”) effects. Not directly in response to the paper, but in conversations about the topic over the past few months, several people have said to us things to the effect of “I prefer fixed effects over random effects because I care about identification.” Neither Drew nor I has any idea what this comment is supposed to mean. Have you come across someone saying something like this? Do you have any thoughts about what these people could possibly mean? I want to respond to this concern when people raise it, but I have failed thus far to inquire what is meant and so do not know what to say.
I have a “cultural” reply, which is that so-called fixed effects are thought to make fewer assumptions, and making fewer assumptions is considered a generally good thing that serious people do, and identification is considered a concern of serious people, so they go together. Maybe there is more going on, though. Let’s see what the blog commenters have to say.
P.S. See also this paper with Joe Bafumi. I generally prefer for my varying coefficients to be modeled. I’m no fan of so-called fixed effects identification. It’s just another model, just not as flexible as the multilevel version.