Welcome to ruspy’s documentation!

Ruspy is an open-source software package for estimating and simulating an infinite horizon single agent discrete choice model in the setting of Rust (1987). This package offers to choose whether to estimate the model using the nested fixed point algorithm suggested by Rust (1987) or by employing the mathematical programming with equilibrium constraints based on Su and Judd (2012). It serves as a foundation for teaching and research in this particular model and can be used freely by everyone. For a full understanding of the mechanisms in this package it is advisable to first read the two papers:

Rust, J. (1987). Optimal replacement of GMC bus engines: An empirical model of Harold Zurcher. Econometrica, 55 (5), 999-1033.

Su, C. L., & Judd, K. L. (2012). Constrained optimization approaches to estimation of structural models. Econometrica, 80 (5), 2213-2230.

and the documentation provided by John Rust on his website:

Rust, J. (2000). Nested fixed point algorithm documentation manual. Unpublished Manuscript.

as well as the comment by Iskakhov et al. (2016) on Su and Judd (2012):

Iskhakov, F., Lee, J., Rust, J., Schjerning, B., & Seo, K. (2016). Comment on “constrained optimization approaches to estimation of structural models”. Econometrica, 84 (1), 365-370.

So far, there has been only one research project based on this code. Numerical experiments for a robust decision rule for Harold Zurcher can be found in this online organisation.

ruspy can be installed via conda with:

$ conda config --add channels conda-forge
$ conda install -c opensourceeconomics ruspy