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DC-EGM

Continuous Integration Workflow Codecov pre-commit.ci status Black

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).

References

  1. Iskhakov, Jorgensen, Rust, & Schjerning (2017). The Endogenous Grid Method for Discrete-Continuous Dynamic Choice Models with (or without) Taste Shocks. Quantitative Economics
  2. Christopher D. Carroll (2006). The method of endogenous gridpoints for solving dynamic stochastic optimization problems. Economics Letters
  3. Loretti I. Dobrescu & Akshay Shanker (2022). Fast Upper-Envelope Scan for Discrete-Continuous Dynamic Programming.