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A few conversations with colleagues who teach econometrics have convinced me that, for all the advanced technical knowledge we impart students in standard econometrics classes, we often don’t do a very good job of teaching them how to interpret what they are estimating. This is generally the reason why I teach a graduate class on the practice of econometrics (i.e., so-called cookbook econometrics class) every other year.

More specifically, this leads me to discussing the interpretation of certain types of coefficients for this week’s installment of ‘Metrics Mondays. Beyond the (accurate) interpretation of coefficients, I don’t have a grand overarching theme, so what follows is a collection of bullet points more than anything.

Jeff Bloem, whose initial post caused me to write my own post a few weeks ago about whether more academics should blog, forwarded my post to a colleague of mine and mentor of his at MSU. My colleague was kind enough to cc me on his reply to Jeff:

Thanks for sending! I disagree with Marc at the margin. He has a good point that there’s an important self-selection factor that no doubt gives an upward bias to the effect of blogging on careers …

But my gut says that Marc misses one important point: There is a generational bias effect too. Few from my generation are blogging, yet some could do so well. Our colleague [Redacted] is a frenetic emailer. Had he started his career 20 years later, I suspect he would be blogging and have a meaningful following. …

My response, in which I make a point (in bold) I don’t see often in discussions of whether academics should blog:

Rob further writes:

I am not an econometrician–I spend my time playing with CGE models–but have to know something about econometrics. Recently I have been reviewing draft papers on a project using detailed tax data in my country–firm-level, matched with individual returns of employees, valued-added tax, import duties, etc.–for the period 2009-2014. A massive and rather unusual database. …

[M]ost of the papers do standard stats, comparing means between different sub groups, running a few regressions comparing some outcome across sectors etc. To me this seems not to do as much with the data as could be done. I kind of feel that one should be able to compare whole distributions, rather than some summary stats. But I am not sure what methods there are for that.

Another interesting–and important–question. There are methods to compare whole distributions. Let’s cover a few of those methods.