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Python package for the simulation and estimation of a prototypical infinite-horizon dynamic discrete choice model based on Rust (1987)

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updates: - [github.com/pre-commit/pre-commit-hooks: v4.3.0 → v4.4.0](https://github.com/pre-commit/pre-commit-hooks/compare/v4.3.0...v4.4.0) - [github.com/asottile/pyupgrade: v3.2.2 → v3.3.0](https://github.com/asottile/pyupgrade/compare/v3.2.2...v3.3.0) - [github.com/PyCQA/flake8: 5.0.4 → 6.0.0](https://github.com/PyCQA/flake8/compare/5.0.4...6.0.0)

We want to separate model and estimation code, to make ruspy more adaptive to different estimation packages. This means we want to eliminate all calls of optimize functions like `estimagic.minimize`...

We want to explore the ideas outlined in [Jorgensen (2019)](https://www.ifs.org.uk/uploads/CWP1620-Sensitivity-to-Calibrated-Parameters.pdf).

master-thesis

Once BHHH is in estimagic, we need to adjust promotional material, to promote full replication of Rust (1987) as well as Su and Judd's MPEC performance comparision.

Add bootstrapping functionality again using estimagic as soon as they merged their [bootstrap PR](https://github.com/OpenSourceEconomics/estimagic/pull/148).

We seem to implement the smooth ambiguity model as outlined in [Marinacci (2015)](https://onlinelibrary.wiley.com/doi/epdf/10.1111/jeea.12164) and estimate its parameters.

master-thesis

In the original algorithm, the algorithmic parameters for switching between NK and contractions iterations were changed after each run. This is a next step for ruspy and also the possibility...