Version Notes

These notes will only include major changes.


  • Overhauled micro moment API

  • Product-specific demographics

  • Passthrough calculations

  • Added problem results methods to simulation results

  • Profit Hessian computation

  • Checks of pricing second order conditions

  • Newton-based methods for computing equilibrium prices

  • Large speedups for supply-side and micro moment derivatives

  • Universal display for fixed point iteration progress

  • Support adjusting for simulation error in moment covariances


  • Refactored micro moment API

  • Custom micro moments

  • Properly scale micro moment covariances

  • Pickling support


  • Elasticities and diversion ratios with respect to mean utility

  • Willingness to pay calculations


  • Simplify micro moment API

  • Second choice or diversion micro moments

  • Add share clipping to make fixed point more robust

  • Report covariance matrix estimates in addition to Cholesky root

  • Approximation to the pure characteristics model

  • Add option to always use finite differences


  • More control over matrices of instruments

  • Split off fixed effect absorption into companion package PyHDFE

  • Scrambled Halton and Modified Latin Hypercube Sampling (MLHS) integration

  • Importance sampling

  • Quantity dependent marginal costs

  • Speed up various matrix construction routines

  • Option to do initial GMM update at starting values

  • Update BLP example data to better replicate original paper

  • Lognormal random coefficients

  • Removed outdated default parameter bounds

  • Change default objective scaling for more comparable objective values across problem sizes

  • Add post-estimation routines to simplify integration error comparison


  • Micro moments that match product and agent characteristic covariances

  • Extended use of pseudo-inverses

  • Added more information to error messages

  • More flexible simulation interface

  • Alternative way to simulate data with specified prices and shares

  • Tests of overidentifying and model restrictions

  • Report projected gradients and reduced Hessians

  • Change objective gradient scaling

  • Switch to a lower-triangular covariance matrix to fix a bug with off-diagonal parameters


  • Support more fixed point and optimization solvers

  • Hessian computation with finite differences

  • Simplified interface for firm changes

  • Construction of differentiation instruments

  • Add collinearity checks

  • Update notation and explanations


  • Optimal instrument estimation

  • Structured all results as classes

  • Additional information in progress reports

  • Parametric bootstrapping of post-estimation outputs

  • Replaced all examples in the documentation with Jupyter notebooks

  • Updated the instruments for the BLP example problem

  • Improved support for multiple equation GMM

  • Made concentrating out linear parameters optional

  • Better support for larger nesting parameters

  • Improved robustness to overflow


  • Estimation of nesting parameters

  • Performance improvements for matrix algebra and matrix construction

  • Support for Python 3.7

  • Computation of reasonable default bounds on nonlinear parameters

  • Additional information in progress updates

  • Improved error handling and documentation

  • Simplified multiprocessing interface

  • Cancelled out delta in the nonlinear contraction to improve performance

  • Additional example data and improvements to the example problems

  • Cleaned up covariance estimation

  • Added type annotations and overhauled the testing suite


  • Estimation of a Logit benchmark model

  • Support for fixing of all nonlinear parameters

  • More efficient two-way fixed effect absorption

  • Clustered standard errors


  • Patsy- and SymPy-backed R-style formula API

  • More informative errors and displays of information

  • Absorption of arbitrary fixed effects

  • Reduction of memory footprint


  • Improved support for longdouble precision

  • Custom ownership matrices

  • New benchmarking statistics

  • Supply-side gradient computation

  • Improved configuration for the automobile example problem


  • Initial release