[grayclhn]

I work on research close to this area -- time series w/ macro applications, but not on these specific models. These specific models are very very important for estimating the effects of monetary policy decisions, forecasting, etc. because they embed a fair bit of economic theory with a decent amount of estimation theory so that the models can address questions about "causality" in a meaningful way: if I intervene by changing the interest rate, what is the distribution of outcomes that will result. (The stuff I work on uses less economic theory so it can't address those questions.)

That's not to say that the NY Fed takes the model's recommendations uncritically and assumes that the models are perfect or the recommendations are the truth, because that would be nuts. Just that this class of models is one of the few ways to use "the data" to provide guidance about policy decisions.

The people who've developed the NY Fed's DSGE model are some of the absolute top people working on these models. It's based on a lot of academic research and a lot of research published in top journals by people at the NY Fed involved in building the model. Other banks have their own models, but I suspect this one is the highest profile. (Again, not precisely my field of research, so I could be missing one or two.)

So, to get around to answering your question. For academic research in economics, this is as "production level" as it gets. Is this the same as using Julia for algorithmic trading? No. The NY Fed's not running this model in real time, and if something crashes then they can restart it. But I think this is huge for Julia's adoption in economic research.

(For a pdf with more about the modeling strategy itself: https://www.newyorkfed.org/medialibrary/media/research/staff... but that is 2 years old now.)