Posts

Festivus Miracle

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Guest post by Jonathan Roth Many interesting questions in economics involve the causal effect of a treatment that was not randomly assigned. Luckily, empirical researchers often find creative ways to circumvent endogeneity issues.  One way to get creative is to use an instrumental variable (IV). Consider the following example: we want to know the causal effect of attending a private school (X) on test scores (Y). Define the potential outcomes of a student attending (X=1) and not attending (X=0) a private school as Y(1) and Y(0), respectively. The individual-level causal effect of attending versus not attending is the difference in potential outcomes, Y(1)-Y(0). With heterogeneity in the treatment effects, this difference will vary across all students.  If we suspect that choice of school is endogenous, we can instrument for X using information on whether you were offered a voucher that reduces the cost of private school (Z). For Z to be a valid instrument, it must obey certain properti

Can't Hack It!

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As the academic year from hell reaches completion, the sun emerges, and the ability to go on vacation resumes, we should all have a little spring in our step and a growing sense of optimism. Then comes a new NBER WP that puts a damper in the moods of many people.   Aside from the topic itself, many comments have focused on the Instrumental Variable (IV) strategy used in the paper.  Forgetting about the specific model at hand, the authors are perfectly upfront about the fact that two instruments are considered, but only one is chosen after seeing the results of some specification tests. And that brings me to the subject of this blog that I have wanted to write for a while, but lacked the mental bandwidth to do so. The issue at hand is pre-test bias; a subject I have written about here and here . The notion is simple, but unfortunately is not well understood it seems by many applied researchers. I, too, was grossly deficient in my understanding coming out of graduate school. It wasn

(Rational) Addictions

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 I have an addiction. Perhaps more than one. But one for sure. And, so, when this image made its rounds this week on Twitter with everyone adding their favorite econometric caption to what this person is winking about, I could not resist. I tried. I failed. I relented. I tweeted what I think she is saying: "No measurement error." Low-hanging fruit, I know. Still, I feel compelled to shout "measurement error" until researchers listen more.  Then, as only an enabler can do, my addiction was fed when  Carlos Carpio Ochoa tweeted a response asking for a blog post on addressing measurement error and endogeneity in a binary covariate, such as treatment assignment. Of course, I do not possess the will power to resist. So, here we are. In a very early post  I discuss the difficulties created with measurement error in an otherwise exogenous binary covariate. Simulations show how an apparently valid instrument does not yield consistent estimates of the true coefficient in

Lipstick on a Pig

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I have never been very into magic or illusions. Even as a child I did not really care. Magic is the original (or at least a much older version of) "fake news". Even if I don't understand how the trick was done, I know it is a trick. While I can appreciate -- I guess -- the skill in pulling off the trick, I don't care enough to really watch. And, I'm certainly never convinced that magic is real. While magic is not real, how does it work? The most common "trick" employed is distraction. The magician needs to distract your eyes. Look here! No, not there! While your eyes are focused where the magician wants, the magician is free to manipulate things elsewhere without scrutiny. I have been thinking about the process of magic a little bit lately ... when I have a free brain cell or two to devote to econometrics. How does magic connect to econometrics, you ask? Well, I'm sure there are plenty of potential answers, but the connection that has been floating i

Nonclassical Classics

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#EconTwitter once again showed this week why it is not a giant waste of time. In a Twitter discussion  started by the wonderful author of The MixTape himself (available here for the low, low price of $35.00), I became aware of a purported phenomenon discovered in psychology over 20 years ago: the Dunning-Kruger Effect. And, given how long 2020 has lasted, it's really about 100 years old. The Dunning-Kruger Effect was the subject of an article and another blog post this week and that sparked Scott's attention. This, in turn, led Grant McDermott , to suggest I enter the fray. See, the aforementioned blog post made its point using R and, since Grant and I are statistical software nemeses, he asked for my Stata take. Of course, I was easily intrigued because it turns out at the heart of the alleged Dunning-Kruger Effect lies measurement error. Nonclassical measurement error to boot! So, I threw all my other work to side and dove right in. I am a sucker for a good measurement err