Fair point re use cases! My familiarity with DSGE models is about a decade out-of-date, so maybe things have improved, but a lot of the wariness then was that typical representative-agent DSGE isn’t great where agent heterogeneity and interactions are important to the dynamics of the system, and/or agents fall significantly short of the rational expectations benchmark, and that in those cases you’d plausibly be better of using agent-based models (which has only become easier in the intervening period).
I (weakly) believe this is mainly because econometrists mostly haven’t figured out that they can backpropagate through complex models
Plausible. I suspect the suspicion of fitting more complex models is also influenced by the fact that there’s just not that much macro data + historical aversion to regularisation approaches that might help mitigate the paucity of data issues + worries that while such approaches might be ok for the sort of prediction tasks that ML is often deployed for, they’re more risky for causal identification.
Yeah, this all sounds right. Personally, I typically assume both heterogenous utilities and heterogenous world-models when working with DSGE, at which point it basically becomes an analytic tool for agent-based models.
Fair point re use cases! My familiarity with DSGE models is about a decade out-of-date, so maybe things have improved, but a lot of the wariness then was that typical representative-agent DSGE isn’t great where agent heterogeneity and interactions are important to the dynamics of the system, and/or agents fall significantly short of the rational expectations benchmark, and that in those cases you’d plausibly be better of using agent-based models (which has only become easier in the intervening period).
Plausible. I suspect the suspicion of fitting more complex models is also influenced by the fact that there’s just not that much macro data + historical aversion to regularisation approaches that might help mitigate the paucity of data issues + worries that while such approaches might be ok for the sort of prediction tasks that ML is often deployed for, they’re more risky for causal identification.
Yeah, this all sounds right. Personally, I typically assume both heterogenous utilities and heterogenous world-models when working with DSGE, at which point it basically becomes an analytic tool for agent-based models.