Macroeconomics

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.

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 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.