Moreover, quant-econ, an academic entity dedicated to scientific programming in the social sciences has a few notebooks in python and Julia [2]
Is there some way to play with these models? Or do you need lots of input data, which isn't freely available?
The reason that is is that purely statistical/predictive models of macro are subject to the Lucas Critique [1]. It says roughly, if you observe an economic relationship is happening, but you don't have an explanatory reason why it is, it's a bad idea to use it for policy prescription.
The NY fed model has a whitepaper here [2], which should be accessible to the technical data scientist (technical, but not prohibitively so).
Their readme points to a few posts on using it, I think the open source code comes with a csv for example input data. There should be publicly accessible macroeconomic data in a few places for you to play with it, say at FRED or the World Bank. I think Julia has a Stata-style api package for FRED data, making the data processing easier.
Have fun!
[1]https://en.m.wikipedia.org/wiki/Lucas_critique
[2] https://www.google.ca/url?sa=t&source=web&rct=j&url=https://...
The estimation however, is usually based on Bayesian hierarchical models, taking the constraints imposed by the theoretical model, I believe.
Here is a good source for that https://www.newyorkfed.org/medialibrary/media/research/staff...
DSGE have been criticized for their low predictive power, but it can be said that in that for mid to long term predictions they are more robust for monetary and fiscal policy use.
That's fine, but I prefer my models to be language-independent.