The Climate in Climate Economics 

(Deep Learning for Integrated Assessment Models)

Machine Learning for Dynamic Incentive Problems

Deep Equilbrium Nets

Building Interpretable Climate Emulators for Economics

Deep Surrogate for Finance:

With an Application to Option Pricing

Self-Justified Equilibria: Existence and Computation

High-dimensional Dynamic Stochastic Model Representation

Sparse Grids for Dynamic Economic Models 

SQUIC

SQUIC is a second-order, L1-regularized maximum likelihood method for performant large-scale sparse precision matrix estimation. The presented source code is the SQUIC shared library (libSQUIC) intended for Linux and Mac OS, and with APIs to Python, R, and C++.

• Link to the Code Repository 

• Link to the paper: "Large-scale Sparse Inverse Covariance Matrix Estimation"

• Link to paper: "Block-enhanced precision matrix estimation for large-scale datasets"

• Link to manual: Algorithm 1042: Sparse Precision Matrix Estimation with SQUIC

Can Today’s and Tomorrow’s World Uniformly Gain from Carbon Taxation?

Replication code to the paper

• Link to the Code Repository 

• Link to the paper: "Can Today’s and Tomorrow’s World Uniformly Gain from Carbon Taxation?"