Econometrics

I felt like I barely did any serious reading this year, and maybe that’s even true, but my read folder contains 168 papers for 2023, so even subtracting the ones that are in there by mistake, that’s enough to pick a few highlights. As usual, I hesitate to call these favorites, but I learned something from them. They are in no particular order except chronological by when I read them.

A list of 10 papers I read and liked in 2021. As in previous years, this is by date read rather than released or published, and selection is in no particular order. Overall, my list reflects my interests this year, prompted by research and teaching, in online learning, micro-founded macro, and causal inference, and, to the extent possible, intersections of these areas. As usual, I’m likely to have missed a lot of great work even in areas on which I focus, so absence likely indicates that I didn’t see it, or it’s on my ever expanding to read list, so ping me with your recommendations!

The following is a look back at my reading for 2020, identifying a totally subjective set of the top 10 papers I read this year. My reading patterns, as usual, have not been so systematic, so if your brilliant work is missing it either slipped past my attention or is living in an ever-expanding set of folders and browser tabs on my to-read list. I’ll exclude papers I refereed, for privacy purposes (a fair amount if you include conferences and also cutting out a lot of the macroeconomics from my list).

Prompted by a question on the slack for Turing.jl about when to use which Bayesian sampling algorithms for which kinds of problems, I compiled a quick off-the-cuff summary of my opinions on specific samplers and how and when to use them. Take these with a grain of salt, as I have more experience with some than with others, and in any case the nice thing about a framework like Turing is that you can switch out samplers easily and test for yourself which is best for your application.

Jérémy Fouliard, Michael Howell, and Hélène Rey have just released an update of their working paper applying methods from the field of Online Learning to forecasting of financial crises, demonstrating impressive performance in a difficult forecasting domain using some techniques that appear to be unappreciated in econometrics. Francis Diebold provides discussion and perspectives. This work is interesting to me as I spent much of the earlier part of this year designing and running a course on economic forecasting which attempted to offer a variety of perspectives beyond the traditional econometric approach, including that of Online Learning.

Inspired by Paul Goldsmith-Pinkham and following on Noah Smith and others in an end of-year tradition, here is a not-quite-ordered list of the top 10-ish papers I read in 2017. I read too many arxiv preprints and older papers to choose ones based on actual publication date, so these are chosen from the “Read in 2017” folder of my reference manager, which tells me that I have somehow read 176 papers (so far) in 2017.

So, inspired by Brian and the general spirit of end-of-year reflection, some thoughts on what I’ve read this year. According to my reference manager software, I’ve read 183 papers this year, which is somewhat overstated because many were read last year but are dated incorrectly, and a substantial portion of the list contains slides, lecture notes, or other documents not quite meriting the status of article.

Inspired by the release of a new and quite clear explainer on the topic by Hahn, Kuersteiner, and Mazzocco (HKM) amid a growing trend of using microeconomic data to learn about macroeconomic or aggregate effects, I believe it’s a good time to write something about what microeconometricians and applied microeconomists ought to know about dealing with aggregate effects. Broadly, this refers to any time-dependent variability in a data-generating process that can’t be modeled is independent across individual observations.

Attention Conservation Notice: Over 5000 words about math that I don’t particularly understand, written mostly to clarify my thoughts. A reader familiar with the topic (roughly, spectral or harmonic theory on graphs and manifolds) will find little here new except possibly misconceptions, while a reader not familiar with the topic will find minimal motivation and poorly explained jargon. The ideal reader is a pedant or troll who can tell me why I’m wrong about everything.