Digifesto

Last week I spoke at PyData NYC 2023 about “Computational Open Source Economics: The State of the Art”.

It was a very nice conference, packed with practical guidance on using Python in machine learning workflows, interesting people, and some talks that were further afield. Mine was the most ‘academic’ talk that I saw there: it concerns recent developments in computational economics and what that means for open source economics tooling.

The talk discussed DYNARE, a widely known toolkit for representative agent modeling in a DSGE framework, and also more recently developed packages such as QuantEcon, Dolo, and HARK. It then outline how dynamic programming solutions to high-dimensional heterogeneous agent problems have ran into computational complexity constraints. Then, excitingly, how deep learning has been used to solve these models very efficiently, which greatly expands the scope of what can be modeled! This part of the talk drew heavily on Maliar, Maliar, and Winant (2021) and Chen, Didisheim, and Scheidegger (2023).

The talk concluded with some predictions about where computational economics is going. More standardized ways of formulating problems, coupled with reliable methods for encoding these problems into deep learning training routines, is a promising path forward for exploring a wide range of new models.

Slides are included below.

References

Chen, H., Didisheim, A., & Scheidegger, S. (2021). Deep Surrogates for Finance: With an Application to Option Pricing. Available at SSRN 3782722.

Maliar, L., Maliar, S., & Winant, P. (2021). Deep learning for solving dynamic economic models. Journal of Monetary Economics, 122, 76-101.