Python implementation of the Discrete-Continuous Endogenous Grid Method (DC-EGM) for solving dynamic stochastic lifecycle models of continuous (e.g. consumption-savings) and additional discrete choices.
The solution algorithm employs an extension of the Fast Upper-Envelope Scan (FUES) from Dobrescu & Shanker (2022).
- Iskhakov, Jorgensen, Rust, & Schjerning (2017). The Endogenous Grid Method for Discrete-Continuous Dynamic Choice Models with (or without) Taste Shocks. Quantitative Economics
- Christopher D. Carroll (2006). The method of endogenous gridpoints for solving dynamic stochastic optimization problems. Economics Letters
- Loretti I. Dobrescu & Akshay Shanker (2022). Fast Upper-Envelope Scan for Discrete-Continuous Dynamic Programming.