Applied Econometrics

I really enjoy Marc Bellemare's applied econ posts, but I really enjoy his econometric related posts (for instance a while back he wrote some really nice posts related  linear probability models   here and here).

Recently he wrote a piece entitled "In defense of the cookbook  approach to econometrics." At one point he states:

"The problem is that there is often a wide gap between the theory and practice of econometrics. This is especially so given that the practice of econometrics is often the result of social norms..."

He goes on to make the point that 'cookbook' classes should be viewed as a complement, not a supplement to theoretical econometrics classes.

It is the gap between theory and practice that has given me a ton of grief as a practitioner. The amount of grief has varied depending on if work was directed strictly toward problem solving or publication (i.e. bowing to the seemingly eccentric whims and wishes of a reviewer). You can spend hours in graduate school working through tons of theorems and proofs or have a mis-spent youth toughing your way through the literature and still find your self on what feels like another planet when it comes to actually doing econometrics. At every corner you will find a different challenge that seems at odds with everything you may have learned in your econometric theory course, textbook, or paper that everyone else cites in their work. As Peter Kennedy states in the applied econometrics chapter of his popular A Guide to Econometrcs, "econometrics is much easier without data".

He states further:

"In virtually every econometric analysis there is a gap, usually a vast gulf, between the problem at hand and the closest scenario to which standard econometric theory is applicable....the issue here is that in their econometric theory courses students are taught standard solutions to standard problems, but in practice there are no standard problems...Applied econometricians are continually faced with awkward compromises..."


The hard part for the recent graduate or new practitioner that has not had a good applied econometrics course or been exposed to the social norms and practices of applied work is figuring out how to compromise, or which sins are more forgivable or harmless than others. They may not no that sometimes you should Do Both and the difference between a garden of forking paths and sensitivity analysis.

As Angrist and Pischke state: "if applied econometrics were easy theorists would do it."


See also:

Culture War: Classical Statistics vs. Machine Learning

Ambitious vs. Ambiguous Modeling

Mostly Harmless Econometrics as an off-road backwoods survival manual for practitioners.

***updated and revised 6/22/2024