cupid_matching 1.0.5

Solves, simulates, and estimates separable matching TU models

cupid_matching

A Python package to solve, simulate and estimate separable matching models

• Free software: MIT license
• Documentation: https://bsalanie.github.io/cupid_matching
• See also: An interactive Streamlit app

Installation

pip install [-U] cupid_matching

Importing functions from the package

For instance:

from cupid_matching.min_distance import estimate_semilinear_mde

Examples

• example_choosiow.py shows how to run minimum distance and Poisson estimators on a Choo and Siow homoskedastic model.
• example_nestedlogit.py shows how to run minimum distance estimators on a two-layer nested logit model.

Warnings

• many of these models (including all variants of Choo and Siow) rely heavily on logarithms and exponentials. It is easy to generate examples where numeric instability sets in.
• as a consequence, the numeric versions of the minimum distance estimator (which use numerical derivatives) are not recommended.
• the bias-corrected minimum distance estimator (corrected) may have a larger mean-squared error and/or introduce numerical instabilities.

Release notes

version 1.0.4

• added an optional bias-correction for the minimum distance estimator in the Choo and Siow homoskedastic model, to help with cases when the matching patterns vary a lot across cells.
• added two complete examples: example_choosiow.py and example_nestedlogit.py.

version 1,0.5

• simplified the bias-correction for the minimum distance estimator in the Choo and Siow homoskedastic model.