Skip to content
BLP Demand Estimation with Python 3
Python
Branch: master
Clone or download
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
docs API/DOC: scale the 2SLS weighting matrix by N to give more reasonable… Aug 22, 2019
pyblp API/DOC: scale the 2SLS weighting matrix by N to give more reasonable… Aug 22, 2019
tests API/DOC: remove N^2 scaling and change default optimizer Jul 2, 2019
.gitignore DOC: switch to notebook-based examples Oct 6, 2018
LICENSE.txt DOC: update license year Apr 25, 2019
MANIFEST.in BLD: update pruned directories in manifest Dec 9, 2018
README.rst DOC: describe how to call pyblp from other languages Jun 2, 2019
readthedocs.yml DOC: add support for PDF version of docs Oct 7, 2018
requirements.txt ENH: rely on better performance of new matmul ufunc Feb 3, 2019
setup.cfg
setup.py BLD/DOC: unpin docs requirements Apr 25, 2019
tox.ini TST: ignore persistent pending deprecation warning Apr 27, 2019

README.rst

pyblp

docs-badge pypi-badge downloads-badge python-badge license-badge

An overview of the model, examples, references, and other documentation can be found on Read the Docs.

The pyblp package is a Python 3 implementation of routines for estimating the demand for differentiated products with BLP-type random coefficients logit models. This package was created by Jeff Gortmaker in collaboration with Chris Conlon.

Development of the package has been guided by the work of many researchers and practitioners. For a full list of references, including the original work of Berry, Levinsohn, and Pakes (1995), refer to the references section of the documentation.

Citation

If you use pyblp in your research, we ask that you also cite Conlon and Gortmaker (2019), which describes the advances implemented in the package.

Installation

The pyblp package has been tested on Python versions 3.6 and 3.7. The SciPy instructions for installing related packages is a good guide for how to install a scientific Python environment. A good choice is the Anaconda Distribution, since, along with many other packages that are useful for scientific computing, it comes packaged with pyblp's only required dependencies: NumPy, SciPy, SymPy, and Patsy.

However, pyblp may not work with old versions of its dependencies. You can update pyblp's dependencies in Anaconda with:

conda update numpy scipy sympy patsy

You can install the current release of pyblp with pip:

pip install pyblp

You can upgrade to a newer release with the --upgrade flag:

pip install --upgrade pyblp

If you lack permissions, you can install pyblp in your user directory with the --user flag:

pip install --user pyblp

Alternatively, you can download a wheel or source archive from PyPI. You can find the latest development code on GitHub and the latest development documentation here.

Other Languages

Once installed, pyblp can be incorporated into projects written in many other languages with the help of various tools that enable interoperability with Python.

For example, the reticulate package makes interacting with pyblp in R straightforward:

library(reticulate)
pyblp <- import("pyblp")

Similarly, PyCall can be used to incorporate pyblp into a Julia workflow:

using PyCall
pyblp = pyimport("pyblp")

The py command serves a similar purpose in MATLAB:

py.pyblp

Features

  • R-style formula interface
  • Bertrand-Nash supply-side moments
  • Multiple equation GMM
  • Demographic interactions
  • Micro moments
  • Fixed effect absorption
  • Nonlinear functions of product characteristics
  • Concentrating out linear parameters
  • Parameter bounds and constraints
  • Random coefficients nested logit (RCNL)
  • Varying nesting parameters across groups
  • Logit and nested logit benchmarks
  • Classic BLP instruments
  • Differentiation instruments
  • Optimal instruments
  • Tests of overidentifying and model restrictions
  • Parametric boostrapping post-estimation outputs
  • Elasticities and diversion ratios
  • Marginal costs and markups
  • Profits and consumer surplus
  • Merger simulation
  • Custom counterfactual simulation
  • Synthetic data construction
  • SciPy or Artleys Knitro optimization
  • Fixed point acceleration
  • Monte Carlo, product rule, or sparse grid integration
  • Custom optimization and iteration routines
  • Robust and clustered errors
  • Linear or log-linear marginal costs
  • Partial ownership matrices
  • Analytic gradients
  • Finite difference Hessians
  • Market-by-market parallelization
  • Extended floating point precision
  • Robust error handling

Features Slated for Future Versions

  • Fast, "Robust," and Approximately Correct (FRAC) estimation
  • Analytic Hessians
  • Mathematical Program with Equilibrium Constraints (MPEC)
  • Generalized Empirical Likelihood (GEL)
  • Discrete types
  • Pure characteristics model
  • Newton methods for computing equilibrium prices

Bugs and Requests

Please use the GitHub issue tracker to submit bugs or to request features.

You can’t perform that action at this time.