How the (Econometric) Sausage is Made

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...

I am entering a new era on this blog. I am now recycling post titles. Back in 2020, I wrote a post called Chicken or Egg?   The subject of that post was on the importance of specifying a model before specifying the estimator. The subject of this post is different, but no less important. It is a mistake I have seen countless Ph.D. students make, especially when entering the research arena for the first time.  Conversations with said students usually go something like this:           Student: I think I have an idea for a paper.           Elder Researcher: Okay.           Student: But, I'm not sure it will work. My response to this is always the same.         Elder Researcher: What do you mean by "work"? And the student's answer is always the same.         Student: I'm not sure I will find something statistically significant. And ... gotcha. ...

Introverts are special people. Trust me. I know. I am one. Now, not all introverts are the same. However, one thing that nearly all introverts have in common is the desire to avoid, or at least minimize, interactions. We can only take so much.  I was thinking about interactions this week. Not social ones, but rather econometric ones. Not only did I happen to cover this in class this week, but then I came across this tweet  by @StatModeling. It led to a blog post mentioning a recent publication on Covid-19 vaccine effectiveness. I am not interested in this article per se  for the purposes of this post. However, one of the study's authors posted this as an introduction to the blog post Logistic regression  can mean different things. It can mean a regression of the log odds ratio on covariates using Ordinary Least Squares (OLS). It can also, perhaps, refer to a logit model when the outcome is binary (since the odds ratio is undefined in this case). Since the study abo...

In a recent post , I wrote about the choices we make in statistical analysis along a continuum of simple ⇒   complex methods. Methods, of course, can be simple or complex along a number of different dimensions, and methods that seem simple may actually be quite complex and vice versa. However, a recurring theme in a lot of empirical work is the desire to represent complex phenomena with a single number. We do this with estimands, such as we when we represent heterogeneous causal effects with a single average treatment effect on the treated (ATT), for example. But, we also do this quite frequently with data. We measure complex things such as "inequality" with a single number such as the Gini coefficient, or "democracy" using a polity index , or corruption using Transparency International's Corruption Perceptions Index , or "development" using the Human Development Index . When we attempt to boil complex things down to a single number, this creates a num...

Ahhhh, summer has arrived. As department chair, it is a welcome relief. Not that the "endless stream of crap" as I describe it has ceased. But, at least I can deal with it poolside.  Alas, something has already disturbed my summer of contentment. A few colleagues in the SMU business school came to me with some questions regarding the econometrics for a paper they are working on. They noticed something hinky in what they were finding, while following the existing literature. They came to me to try to make sense of it.  If I am right, the issue is very simple, yet reveals a lack of econometric knowledge in the existing literature. Of course, it could be I am the one lacking appropriate knowledge. You, my dedicated readers, can let me know.  Simplifying the issue a bit, the general research question is whether individuals in position of power within a firm (or other organization) matter. The prior literature focuses on managers and CEOs. Do these workers have an "effect...