Rigor and modeling in economics

In a postscript, Andrew Gelman laments a general trend he notices in economics:

My only real problem with it is that when discussing data analysis, [the authors] pretty much ignore the statistical literature and just look at econometrics. In the long run, that's fine--any relevant developments in statistics should eventually make their way over to the econometrics literature. But for now I think it's a drawback in that it encourages a focus on theory and testing rather than modeling and scientific understanding.

Gelman has an idea about why this might be the case:

The problem, I think, is that they (like many economists) think of statistical methods not as a tool for learning but as a tool for rigor. So they gravitate toward math-heavy methods based on testing, asymptotics, and abstract theories, rather than toward complex modeling. The result is a disconnect between statistical methods and applied goals.

There is likely a balance here that Gelman misses between theoretical modeling and statistical modeling. Economists are in the business of testing complex theoretical models. A complex statistical model may draw attention away from that narrow goal.

Not that I necessarily endorse that viewpoint. It simply feels slightly unfair to economists to say that their spartan statistical modeling is a product of their obsession with technical rigor.

Posted by Matt Blackwell at August 30, 2010 9:43 AM