[mlader]

I also come from an Economics background, and am now a software engineer/budding data scientist. As I've delved more into machine learning topics, I'm amazed (though not surprised!) at how both academic and industry economists are still mostly focused on running OLS/logit/probit regressions, and not other classification techniques. My undergraduate thesis did use some computational models that sought convergence for dynamic & stochastic conditions, but that was definitely not the norm.

[tomrod]

Macroeconomics and empirical industrial organization are leading the forefront in terms of theoretical and applied technical advances. You ought to look at discrete choice analysis sometime--great stuff.

I can't speak for industry economists, but the reason we academics tend to spend so much time with OLS/Logit/Probit is their flexibility and scalability.

[mlader]

Macro was my favorite subject! I was lucky enough to take the first year PhD sequence during my last year, which was my first taste of coding =D

I think in industry (anti-trust at least), they stick with the older models because their value has legal precedent, and using new methods would require some more legal hand waving by the attorneys.