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cupid_matching

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

Installation

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pip install [-U] cupid_matching

Importing functions from the package

For instance:

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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.