The Climate in Climate Economics 

(Deep Learning for Integrated Assessment Models)

Using Machine Learning to Compute Constrained Optimal Carbon Tax Rules 

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?"