Deep Learning as a Projection Method for Solving Economic Models

Kenneth Judd and Karl Schmedders

No 35806, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: Deep learning is increasingly presented in economics as a new paradigm for solving dynamic models, superseding the classical projection methods of numerical economics. We argue that this dichotomy rests on a misclassification. The neural-network solvers emphasized in this literature are least-squares projection methods with an adaptive nonlinear parameterization. Placing deep learning inside the projection family reorients the agenda. The issue is not a contest between paradigms, but suggests a single toolkit using a variety of approximation methods, residual criteria, and evaluation designs to be chosen on the merits for the model at hand. In low and moderate dimensions, linear-basis and sparsegrid methods are often more accurate, faster, and easier to verify than a trained neural network. In high dimensions, an appropriate choice for the space of permissible functions will often avoid the curse of dimensionality.

JEL-codes: C60 (search for similar items in EconPapers)
Date: 2026-09
Note: EFG TWP
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.nber.org/papers/w35806.pdf (application/pdf)
Access to the full text is generally limited to series subscribers, however if the top level domain of the client browser is in a developing country or transition economy free access is provided. More information about subscriptions and free access is available at http://www.nber.org/wwphelp.html. Free access is also available to older working papers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nbr:nberwo:35806

Ordering information: This working paper can be ordered from
http://www.nber.org/papers/w35806
The price is Paper copy available by mail.

Access Statistics for this paper

More papers in NBER Working Papers from National Bureau of Economic Research, Inc National Bureau of Economic Research, 1050 Massachusetts Avenue Cambridge, MA 02138, U.S.A.. Contact information at EDIRC.
Bibliographic data for series maintained by ().