Marc F. Bellemare Posts

One of the things I often tell students when discussing whether to use linear regression or a more complicated nonlinear (i.e., maximum likelihood-based) procedure is that one advantage of linear regression is that it prevents identification by functional form.

By “identification via functional form,” what I mean is that the distributional or functional form assumptions made in the context of more complicated nonlinear procedures can lead you to estimate a coefficient which is purely identified because of those distributional or functional form assumptions.

I always had a hard time clearly explaining the intuition behind this, until my colleague Arne Henningsen, with whom I co-taught my advanced econometrics class at the University of Copenhagen, gave a really good example to the class. Here is that example.

Last year I published a post titled “Econ PhDs and the Agricultural and Applied Economics Job Market,” which was pretty popular.

Given that, and after serving as placement director for our department for a few years now, I thought I should write a post that discusses what ag and applied econ PhD students should know when they decide to go on the broader economics job market. Here goes, in no particular order:

In the capharnaum leading to the start of the fall semester, I had somehow lost sight of the idea of posting the newest version of my paper with Casey Wichman in which we derive elasticities for regressions involving the inverse hyperbolic sine (IHS) transformation.

Here it is. The results haven’t changed much, but this version is considerably better, and we are grateful to those who have read the first version. Here is the abstract: