This week’s edition of ‘Metrics Monday will be a slight departure from the usual post in that I won’t be making any specific point about applied work. Rather, this will be more of a meta-post on econometrics focusing on how econometrics is taught.
The way I see it, econometrics has two general objectives:
- Causal inference, and
- Forecasting or properly modeling the data-generating process (DGP).
According to a new working paper by Angrist and Pischke (2017), the way econometrics is taught needs to be rethought, because although many of the problems economists are currently studying involve causal inference, a lot of the tools and the language that is used to teach econometrics to undergraduates (some of whom will go on to learn nothing else in econometrics after that one introductory course) is a holdover from the days when econometrics was all about forecasting or properly modeling the data-generating process.
Among other things, here is what Angrist and Pischke recommend:
