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..."*

As Angrist and Pischke state:

*"if applied econometrics were easy theorists would do it."*

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.

Another issue with applied vs. theoretical econometrics is software implementation. Most economists I know seem to use STATA, but I in the past I primarily worked in a SAS shop and learned R on the side. But most of my coursework in econometrics was done on PAPER with some limited work in SPSS. Statistical programming is a whole new challenge. For example, propensity score matching in SAS is not straight forward (although here 314-2012 is a really nice paper if you are interested and the folks at the Mayo Clinic have a nice set of macros to help out).

What can you do to get a jump start in statistical programming and applied work if you don't have the luxury of someone showing you the ropes? Maybe the best thing you can do is attend some conferences. While not strictly an academic conference, SAS Global Forum has been a great conference with proceedings replete with applied papers with software implementation. R bloggers also offer some good examples of applied work with software implementation.

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 1/24/2019

Re: software implementation: It's also worth looking into Python, especially for those new to applied work. There is a growing network of people using Python and it is a pretty easy language to pick up.

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