How the (Econometric) Sausage is Made

So, it snowed in North Texas over night. There is at least 0.25 inches. Of course, that means all schools are closed for at least today and tomorrow. Likely the day after as well. Seems like a good time to turn on Netflix and write another post. I have wanted to write this one for a while, but it also came up during lecture on Monday. And, that may be the last lecture I give this week. The topic of the lecture was very straightforward: omitted variable bias. All empirical researchers understand omitted variable bias; omission of relevant covariates relegates them to the (composite) error term and biases the estimated coefficients when using Ordinary Least Squares (OLS) if the omitted covariates are correlated with included covariates.  However, we know more than just the fact that OLS is biased in this situation. We also know the formula for the bias. How do we know this? Well, I will tell you what is not  the answer to this question. We do not  answer this question by st...

Being sick and stuck at home makes me cranky. So, seems like the perfect time to write a new post about something that has long irked me. Robustness checks. Khoa Vu has tweeted many times about the absurd number of robustness checks in today's empirical papers. See, for example,  here . Jokes abound about countless robustness requests by the infamous Referee 2, the length of appendices to NBER working papers, and the despair that researchers feel when a good paper comes crashing down due to failure of the 87th robustness check.  In my view, researchers have just accepted this as the new reality when publishing, and we do not stop think about things in sufficient detail. In my view, we need to realize that, just as with penguins, not all robustness checks are created equally. When producing an empirical study, there is no doubt that there are countless decisions that must be made along the way. It is impossible appreciate this until it's your paper. However, Huntington-Klein et...

Ah, grad school. It's brutal, both in terms of the work required and the mental toll. Thankfully, the latter is more out in the open these days. I came across one tweet this week, presumably from a current PhD student, asking how often people thought about dropping out of grad school. Today, I came across another tweet asking how often PhD students were brought to tears. I must admit, I have had a few PhD students cry in my office over the years.  Thinking about the mental toll of grad school for myself, I was reminded of an incident during my time that happens to be (tangentially) related to the topic of this post. It happened when I was attending an empirical seminar given by some outside speaker. No clue who the speaker was. Well, one of the professors (I do recall who but won't name names) could not see how something in the model was identified. It didn't seem like the speaker could get the explanation across; or, the professor wasn't doing a good job listening. It ...

Every parent with young children gets the tv shows their kids watch etched into their permanent memory. No way to get it out of there. For me, one of our kid's favorites was Blue's Clues. A quote from one episode, where Blue is playing hide-and-go-seek, is " I could really use your help. Will you help me? You will? Oh good. Let me know if you see Blue." I am thinking about Blue today for two reasons. First, I just finished updating my lecture notes for Econometrics II, our second-semester econometrics course for first-year economics Ph.D. students (available here ). Second, I came across this  tweet   retweeted by Stephen Wild reminding us all how hard econometrics and statistics are, yet how deficient training can be in graduate school. Truth be told, it's not that graduate training is deficient. Rather, there is just SO MUCH to know.  My lecture notes now exceed 630 slides. For one semester. Yikes! Alas, back to Blue. Most empirical studies even to this day, us...