How the (Econometric) Sausage is Made

It is perhaps fitting that the name of the current pandemic, COVID-19, contains "ID" in its name. It has illuminated a number of statistical issues that researchers have grappled with for decades, if not centuries. But, perhaps the most fundamental issue it has brought to the forefront is identification. In particular, identification of the current infection rate as well as identification of the disease's mortality rate. As I am sure has been articulated better elsewhere, both of these rates are impossible to identify right now without making heroic assumptions about selection into testing, availability of testing, share of asymptomatic cases, ... When point identification is (near) impossible without leaps of "incredible certitude," partial identification approaches scream for attention (Manski 2011). See my previous post here .  Thinking about what this might entail in the current situation brought to mind an old working paper of mine that was never

It's not Christmas and, besides, I'm Jewish. But, we could all use a little holiday reference about now. So, let me tell about how I was reminded of this little guy while teaching econometrics this past week, and again while handling a submission as a journal co-editor. You are probably wondering what Rudolph the Red-Nosed Reindeer has to do with econometrics. Of course, there are a lot of "R"s in his name, but I prefer Stata. So, that's not it. No, I was thinking about poor Rudolph because no one wanted to play any reindeer games with him. In applied econometric research, there are lots of things we could  be playing with, but refuse. And, like Rudolph, I do not understand why. The things I am referring to are model specification tests. This is by no means an area in which I am expert. But there are two simple  specification tests that are applicable to probably the vast majority of empirical (micro) research, yet virtually never make an appearance. Like

As shoppers at the Gap, we take our time and enjoy ourselves. So, too, I hope you enjoy this post at a leisurely pace. This is a gentle way of warning you that this is a long post. But, the Gap plays another role in this post as well. As (social) scientists, we are constantly worrying about gaps , not the Gap . Gaps in our knowledge, mostly. Many new studies identify a knowledge gap in a specific literature and purport to "fill this gap." As empirical researchers, we seek to fill these gaps by bringing rigorous statistical analysis to data. Nearly a century ago, starting with Yule  (1927), this rigorous statistical analysis has been applied to time series data. For more than a half century, starting with Mundlak (1961) and Balestra & Nerlove (1966), this rigorous analysis has been applied to panel (or longitudinal) data. When data involve a time dimension, there is a completely different kind of gap that merits attention, but most often is ignored: the