DSGE models aim to be explanatory models of an economy. They are not the best at predicting (bayesian VARs do better forecasting, there is a tutorial on it in m

DSGE models aim to be explanatory models of an economy. They are not the best at predicting (bayesian VARs do better forecasting, there is a tutorial on it in my quant econ link) but they turn out to do forecasting decently well, too. 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 https://en.m.wikipedia.org/wiki/Lucas_critique [2] https://www.google.ca/url?sa=t&source=web&rct=j&url=https://www.newyorkfed.org/medialibrary/media/research/staff_reports/sr647.pdf&ved=0ahUKEwikuP3S45nQAhUl_4MKHUgPD7wQFggaMAA&usg=AFQjCNGrvKihOfAe268qPnOtRMvn3LG0vw&sig2=r8LBNM_himWcWbvLWfZ2gg https://www.google.ca/url?sa=t&source=web&rct=j&url=https://...