How the (Econometric) Sausage is Made

It's a question nearly as old as time itself, dating back at least to Aristotle.  " Which came first, the chicken or the egg? " Individuals representing different disciplines have arrived at different answers, or no answer at all. I've been thinking about this ancient question lately, and not for the reasons you might think. It would be easy to link this question to questions of causation  that many readers of this blog grapple in their empirical research. Does the egg cause the chicken to exist, or does the chicken cause  the egg to exist. I'm sure if Paul is reading this -- you know which Paul -- his mind is immediately going to what the DAG looks like and, I imagine, concluding that it is not acyclic . Anyway, I digress. This post is not about causation per se . This is about an issue in empirical work that rears its "ugly" head far too often, under different guises, different motivations, different descriptions. But, nonetheless, fundamentally repre

Twitter giveth and Twitter taketh away. For example, it dares us to spend some of our time replying to silly tweets such as "What are the three scariest words in your profession?" or something to that effect. Questions likes this taketh away our time, but giveth us an opportunity to learn in return. The opportunity to learn arises because when questions of the flavor of the one above arise, empirical researchers are often quick to respond something akin to ... Many researchers laugh at such responses, but then secretly fear that this frightening creature is out there, lurking, ready to pounce on their own brilliantly conceived research plan. Clearly, this is in good fun. Except when it's not. Behind every joke is an element of truth. And so these jokes really  bother me. At risk of being a downer, such jokes help statistically significant results  (i.e., instances where the null hypothesis that a parameter is equal to zero are  rejected ) maintain their place atop a pedes

The world is grappling with extremely important issues, both inside academia and economics in particular and outside in the real world. I was struggling with whether to write a post this week and was leaning towards not. Then I came across an interesting Twitter  thread  by Ioana Marinescu which linked to an earlier  thread  by Mar Reguant. I became inspired to write a post for those with the mental bandwidth, or those needing a brief (and partial) respite. The subject of the threads, particularly the one by Mar, is testing for the presence of racism. To understand the issue, let's back up and briefly review what it means (in economics) to conduct a statistical test. Before I do, I would like to acknowledge that I have seen several scholars on Twitter espouse the position that researchers are devoting too much time testing for the presence of racism . People of Color know racism exists. Thus, efforts should instead be directed to finding solutions. I defer to those mo

It's been a difficult few weeks for all of us. But, it has been difficult for many for much longer than that. Many of us are finding it hard to focus on work, too busy wondering what we can do to help and often coming up (relatively) empty. For some, one's research may speak directly to aspects of the issues currently dominating the news. For many others, one's research may seem far less relevant to the current crisis.  That said, regardless of what  the subject of your research is, if you are reading this blog it is because your research is largely empirical in nature. And, if it is empirical in nature, I am a firm believer your research can help depending on how  you conduct your research. I am not even remotely an expert, or even particularly well-informed, about all that ails our society. However, at the most simplistic level, I imagine it is fair to say that our problems relate to deep historical divides between the privileged and the under-privileged, between the ric