Deep learning methods for solving dynamic economic models offer computational advantages but produce ‘black box’ solutions. This paper develops Economics Informed Neural Networks (EINNs) through a generalized method of moderation that embeds behavioral bounds directly into neural network architectures.

Consumer spending lies between pessimistic and optimistic extremes. This paper uses that fact to redefine the search space for optimal consumption rules.

The multinterp Python package offers a unified and versatile solution for multivariate interpolation, addressing the limitations of existing fragmented tools.

Novel framework combining macroeconomic and agent-based financial market modeling to study financial stability with household agents.

Initiative focused on enhancing reproducibility in computational economics research, emphasizing integration of software development and research publication.

Efficient method for solving heterogeneous agent models with multiple decisions, extending the Endogenous Grid Method with Gaussian Process Regression for unstructured grids.

Enhances heterogeneous agent models by integrating life cycle considerations and wealth-dependent utility into structural estimation.

Traditional retirement models suggest spending all savings by end of life. This paper shows new models with wealth in utility provide more realistic guidance.

State-of-the-art computational model of optimal choices for household spending, investing, and home buying. First open source toolkit for this class of models.

Uses a unique stock market model to show how investors with different expectations impact financial stability. Supported by DARPA.