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I just got back from an excellent meeting of the Society for Economic Dynamics, a top conference for work in dynamic economics, principally but not exclusively in macroeconomics. As one of the first in-person conferences I’ve been to since 2020 (last year they were hybrid and I presented from home), it was a chance to catch up not just with colleagues and friends but also with the state of modern academic macro, after some time focusing more on other things.

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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!

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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).

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In this post, I’d like to lay out a few questions and concerns I have about Bayesianism and Bayesian decision theory as a normative theory of inductive inference. As a positive theory, of what people do, psychology is full of demonstrations of cases where people do not use Bayesian reasoning (the entire “heuristics and biases” area), which is interesting but not my target. There are no new ideas here, just a summary of some old concerns which merit more consideration, and not even necessarily the most important ones, which are better covered elsewhere.

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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.

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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.

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Inspired by a request by Jim Savage asking for examples of recent work using heterogeneous agent models, I’ve put together a far from comprehensive list of papers demonstrating the range of work being done using these tools to understand a variety of issues at the intersection of macroeconomics and microeconomic data. While the field has a ways to go in terms of econometric modeling, the best recent work involves much more substantial use of data to discipline results and compare alternative hypotheses.

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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.

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Attention Conservation Notice: Over 2000 words, written for an intended audience of non-economists, describing my thesis, which is supposed to be about Inequality, which you probably care about, but in fact is mostly about algorithms, which maybe only some of you care about. Most of the length is a discussion of rational expectations models which will be old news to economists and slightly bizarre to those who aren’t. Given that I am currently in the midst of completing graduate school and making the transition to other (potentially better) things, I thought it would be a good time to talk about what I’ve been doing with the past, say, 5 to 7 years of my life.

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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.

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