Worth noting that many economists (including e.g. Solow, Romer, Stiglitz among others) are pretty sceptical (to put it mildly) about the value of DSGE models (not without reason, IMHO). I don’t want to suggest that the debate is settled one way or the other, but do think that the framing of the DSGE approach as the current state-of-the-art at least warrants a significant caveat emptor. Afraid I am too far from the cutting edge myself to have a more constructive suggestion though.
First, DSGE models as actually used are usually pretty primitive. I (weakly) believe this is mainly because econometrists mostly haven’t figured out that they can backpropagate through complex models, and therefore they can’t fit the parameters to real data except in some special simple cases. From what I’ve seen, they usually make extremely restrictive assumptions (like Cobb-Douglas utilities) in order to simplify the models.
Second, the use-case matters. We’d expect e.g. financial markets to be a much better fit for DSGE models than entire economies. And personally, I don’t even necessarily consider economies the most interesting use-case—for instance, to the extent that a human is well-modelled as a collection of subagents, it makes sense to apply a DSGE model to a single human’s preferences/decisions. (And same for other biological systems well-modelled as a collection of subagents.)
Anyway, the important point here is that I’m more interested in the cutting edge of mathematical-models-of-collections-of-agents than in forecasting-whole-economies (since that’s not really my main use-case), and I do think DSGE models are the cutting edge in that.
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 My familiarity with DSGE models is about a decade out-of-date, so maybe things have improved, but a lot of the wariness then was that typical representative-agent DSGE isn’t great where agent heterogeneity and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.
Worth noting that many economists (including e.g. Solow, Romer, Stiglitz among others) are pretty sceptical (to put it mildly) about the value of DSGE models (not without reason, IMHO). I don’t want to suggest that the debate is settled one way or the other, but do think that the framing of the DSGE approach as the current state-of-the-art at least warrants a significant caveat emptor. Afraid I am too far from the cutting edge myself to have a more constructive suggestion though.
Two comments on this;
First, DSGE models as actually used are usually pretty primitive. I (weakly) believe this is mainly because econometrists mostly haven’t figured out that they can backpropagate through complex models, and therefore they can’t fit the parameters to real data except in some special simple cases. From what I’ve seen, they usually make extremely restrictive assumptions (like Cobb-Douglas utilities) in order to simplify the models.
Second, the use-case matters. We’d expect e.g. financial markets to be a much better fit for DSGE models than entire economies. And personally, I don’t even necessarily consider economies the most interesting use-case—for instance, to the extent that a human is well-modelled as a collection of subagents, it makes sense to apply a DSGE model to a single human’s preferences/decisions. (And same for other biological systems well-modelled as a collection of subagents.)
Anyway, the important point here is that I’m more interested in the cutting edge of mathematical-models-of-collections-of-agents than in forecasting-whole-economies (since that’s not really my main use-case), and I do think DSGE models are the cutting edge in that.
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.