How the (Econometric) Sausage is Made

Mark Twain famously quipped, "The rep ort of my death was an exaggeration." As with Twain, reports of #EconTwitter's demise has also been grossly exaggerated. Case in point, an interesting econometric question was posed by Casey Wichman . The question concerned testing hypotheses containing parameters estimated from two different specifications (where one specification is estimated using only a subset of the observations used to estimate the other specification ... but that does not change anything).  Several individuals immediately offered a solution: stacked regression .  Hat tip to those responding to Casey, especially Paul Goldsmith-Pinkham 's detailed response. Nonetheless, it seemed like a great topic for a new blog post.  I recall hearing the term -- stacked regression -- discussed during graduate school. Like many things at the time, I did not really understand what it meant. As a young professor, I also often heard discussions about stacking moment conditions

After 2.5 years as department chair, I have found the mental bandwidth to return to my blog on econometric stuff for applied people. Since it is job market season, and many elevator pitches and job talks are about to given, humor me for a short rant on a topic that has long irked me.  The so-called credibility revolution in economics refers to the focus of most empirical research (by academics at least) on credible identification of the causal effects of some policy or intervention or treatment. Whether this revolution has been mostly harmless or sharp  is a matter of perspective , but it has been important. This revolution has emphasized, among other things, that when selection into treatment is not random, researchers must take great care to understand the treatment assignment mechanism in order to apply an econometric solution that yields consistent estimates of one or more causal effect parameters under plausible assumptions.   Much of this work relies on the now well-known poten

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