Should-Read: Why I am starting to think that both statistics and economics are about to come under serious threat from data science: Frances Woolley: Why do beginner econometricians get worked up about the wrong things?: “People make elementary errors when they run a regression for the first time…

…They inadvertently drop large numbers of observations by including a variable, such as spouse’s hours of work, which is missing for over half their sample. They include every single observation in their data set, even when it makes no sense to do so. For example, individuals who are below the legal driving age might be included in a regression that is trying to predict who talks on the cell phone while driving. People create specification bias by failing to control for variables which are almost certainly going to matter in their analysis, like the presence of children or marital status.  But it is rare that I will have someone come to my office hours and ask “have I chosen my sample appropriately?” Instead, year after year, students are obsessed about learning how to use probit or logit models, as if their computer would explode, or the god of econometrics would smite them down, if they were to try to explain a 0-1 dependent variable by running an ordinary least squares regression….

I am happy to concede to Dave Giles that, all else being equal, it is better to use probit than ordinary least squares, and that Stata’s margins command is not that difficult for an undergraduate to use. But all else is not equal. Using probit will not save a regression that combines men and women together into one sample when estimating the impact of having young children on the probability of being employed, and fails to include a gender*children interaction term. (The problem here is that children are associated with a higher probability of being employed for men, and a lower probability of being employed for women. These two effects cancel out in a sample that includes both men and women.) Once students know how to appropriately define a sample, deal with missing values, spot an obviously endogenous regressor, and figure out which explanatory variables to include in their model, then it might be worth having a conversation about the relative merits of probit and linear probability models. Until then, I’m telling my students to use the regress command and, if it makes them feel better, stick “robust” at the end of it. They don’t listen.

It all comes down to the way that they have been taught econometrics…. Econometrics is taught that way for a simple, practical reason: it’s easy. When every student downloads his own data, works on his own unique problem, and specifies a novel and original model, each student will need a lot of individual help and attention. The marking cannot be delegated to a TA, because each research question, and each data set, is different, so it is impossible to write down a simple answer key. But spending hours upon hours reading students’ first struggling steps at regression analysis is a huge amount of work. It’s so much easier to mark a final exam consisting of calculations, short answer questions, and replication of theorems…

Data science venn diagram Google Search


Brad DeLong


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