How the (Econometric) Sausage is Made

Listening to my Spotify playlist this week, several songs by the Gipsy Kings have come up. Clearly my musical preferences have not evolved over time. One peppy song is Bamboleo . Part of the chorus is Bamboleo, bambolea Porque mi vida, yo la prefiero vivir asi In English, this translates to (according to Google anyway) Bamboleo, bambolea Because in my life, I prefer to live it that way You can almost hear the music! Well, there is something else to the song besides the uplifting beat; there are the lyrics. As the song says, individuals typically live life the way they prefer , not the way dictated from on high. Heeding this message is quite useful when it comes to ... the analysis of program or policy effects.  What does this have to do with the empirical analysis of programs or policies? I'm glad you asked. Well, it brings to the fore the important point that just because things are dictated in a certain manner does not preclude a different reality from emerging. Agents may, after

So much of this week was ugly and infuriating.  The suppression of voting rights is real and just, literally, out there in the open. The pandemic has no end in sight and will likely get worse before it gets better. If it gets better.  But, you know what else is ugly and infuriating? Empirical researchers using the wrong tool for the job. One particular example came up in a small discussion on #EconTwitter this week with my part-time antagonist, part-time protagonist.  In this example, let's say we all agree that the true data-generating process (DGP) is Y = a + bX + e. How should one estimate the model? The answer is: ¯\_(ツ)_/¯.  We need more information. But, what information? Probably the first thing that went through your mind is that you need to know whether Cov(X,e) is zero or non-zero. If you are old school, you might also wonder about the properties of e itself. These factors might influence your choice among Ordinary Least Squares (OLS), Generalized Least Squares (GLS), and

In what I guess could only have been done to taunt me after my previous post on not completely dismissing an estimator, this week's Twitter decided to offer a (admittedly humorous) sucker punch at another estimator: propensity score matching (PSM). But, I was thankful for the dig at PSM. As a new department chair, I am a bit worried about having the time and mental energy to keep up my blogging. At the start of the pandemic, I kind of sort of promised to blog every week to help keep the spirits up. Little did I know, five months later, there would still be no end in sight.  Turns out, the dig at PSM was just the kick in the pants I needed to motivate a new post.  PSM and matching in general has drawn the ire of many applied researchers for several years now. As with Instrumental Variables (IV) in my prior post, I am not entirely sure why. And, there is probably a whole new cohort of applied researchers who have no idea why and are too nervous to ask. I think there are a few possib

In a week full of being annoyed or worse at seemingly every turn, I was again confronted by a big pet peeve of mine: the unilateral dismissal by researchers of whichever estimator they happen to find unpalatable for whatever reason.  There are only two reasons to completely dismiss an estimator. First, if the assumptions required of the estimator are strictly stronger than another estimator. Second, the estimator performs very poorly in finite samples even when the assumptions hold. This object of this week's disdain was Instrumental Variables (IV). While I, myself, have warned several times of the temperamental nature of IV on this blog (see here  and here ), IV is a worthy tool in the empiricist's tool kit.   If you need to be convinced, simply simulate some data that correspond to the following data structure and estimate. Here, Y is the outcome, X is the endogenous regressor, U are unobserved determinants of Y, and Z is the instrument. Miraculous! Formally, consistency of I