How the (Econometric) Sausage is Made

My previous post entitled " The Great Divide " has generated a lot of engagement on Twitter and some on Blue Sky. I am very humbled by that. I am humbled that people read any of my blog posts. There has been some push back, some wholehearted agreement, and perhaps some muting of which I am not aware. Nonetheless, given the feedback, I thought I would continue the discussion and (maybe) better frame my thoughts.  Most of the (vocalized) pushback has centered on the steps taken by theorists, particularly since the Credibility Revolution started, to be relevant. Many theorists are attuned to various degrees to applied work. Some theory journals as well as pointed out by  Jaap Abbring, the current managing editor of Econometrics Journal , which describes itself as follows on its website: This is great. Many theory papers are indeed motivated by concrete examples, contain running examples, and may even include an application. Publicly provided code is not uncommon either. I think

I applaud the fact that the American Economic Association, the European Economic Association, the Econometric Society, and the Royal Economic Society have established an ad-hoc joint committee tasked with proposing ways to improve the publication process in economics. I encourage everyone to fill out the survey  (limited to members of the AEA, EEA, ES, and RES, however) even though I am quite cynical about what may come of it. Although only tangentially related, I decided to write a post about a topic that vexes me (and others to whom I have spoken, but I will leave it to them to out themselves) at the intersection of the Venn diagram containing publishing (editing and refereeing), teaching, and econometrics.   What lies at this intersection is the great divide between theoretical and applied econometrics, both in the classroom and in the wild.  As I have progressed in my career, I have enjoyed writing more econometrics papers and fewer economics papers per se . However, when

The world is a very different place from when I started this blog. The pandemic is over ... but I'm not sure we are in a better place. Different, not necessarily better. This brave new world has made it hard for me to find the time and mental energy to continue this blog. Moreover, with the changes at #EconTwitter ( X ) , no one may read this anyway. Nonetheless, I move forward and we shall see if this post (and any future posts) provide utility to anyone beyond myself.  So, I have spent some time thus far this summer revising my lecture notes (available here  ... although it is still in progress for some). I still stand firm in my belief that all empirical researchers ought to teach econometrics (if for no other reason as a constant reminder that there is more to the field than diff-in-diff). As I did so, I added two new extensions to my discussion of -- what else -- measurement error. But, this post is not about measurement error per se. Really. No, I mean it. Instead, it is abou