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Solves, simulates, and estimates separable matching TU models

Project description


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


pip install [-U] cupid_matching

Importing functions from the package

For instance:

from cupid_matching.min_distance import estimate_semilinear_mde


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


  • 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: and

version 1,0.5

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

version 1.0.6

  • corrected typo.

version 1.0.7

  • fixed error in bias-correction term.

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