PyBLP

June 9, 2025 · View on GitHub

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.. description-start

An overview of the model, examples, references, and other documentation can be found on Read the Docs <https://pyblp.readthedocs.io/en/stable/>_.

.. docs-start

PyBLP 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 <https://jeffgortmaker.com/>_ in collaboration with Chris Conlon <https://chrisconlon.github.io/>_.

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) <https://ideas.repec.org/a/ecm/emetrp/v63y1995i4p841-90.html>, refer to the references <https://pyblp.readthedocs.io/en/stable/references.html> section of the documentation.

Citation

If you use PyBLP in your research, we ask that you also cite Conlon and Gortmaker (2020) <https://jeffgortmaker.com/files/Best_Practices_for_Differentiated_Products_Demand_Estimation_with_PyBLP.pdf>_, which describes the advances implemented in the package. ::

@article{PyBLP,
    author={Conlon, Christopher and Gortmaker, Jeff},
    title={Best practices for differentiated products demand estimation with {PyBLP}},
    journal={RAND Journal of Economics},
    volume={51},
    number={4},
    pages={1108-1161},
    year={2020}
}

If you use micro moments with PyBLP, we ask that you also cite Conlon and Gortmaker (2025) <https://jeffgortmaker.com/files/Incorporating_Micro_Data_into_Differentiated_Products_Demand_Estimation_with_PyBLP.pdf>_, which describes the standardized framework implemented by PyBLP for incorporating micro data into BLP-style estimation. ::

@article{MicroPyBLP,
    author={Conlon, Christopher and Gortmaker, Jeff},
    title={Incorporating micro data into differentiated products demand estimation with {PyBLP}},
    journal={Journal of Econometrics},
    pages={105926},
    year={2025}
}

Installation

The PyBLP package has been tested on Python <https://www.python.org/downloads/>_ versions 3.6 through 3.9. The SciPy instructions <https://scipy.org/install/>_ for installing related packages is a good guide for how to install a scientific Python environment. A good choice is the Anaconda Distribution <https://www.anaconda.com/download>, since it comes packaged with the following PyBLP dependencies: NumPy <https://numpy.org/>, SciPy <https://scipy.org/>, SymPy <https://www.sympy.org/en/index.html>, and Patsy <https://patsy.readthedocs.io/en/latest/>. For absorption of high dimension fixed effects, PyBLP also depends on its companion package PyHDFE <https://github.com/jeffgortmaker/pyhdfe>, which will be installed when PyBLP is installed.

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

conda update numpy scipy sympy patsy

You can update PyHDFE with::

pip install --upgrade pyhdfe

You can install the current release of PyBLP with pip <https://pip.pypa.io/en/latest/>_::

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 <https://pypi.org/project/pyblp/>. You can find the latest development code on GitHub <https://github.com/jeffgortmaker/pyblp/> and the latest development documentation here <https://pyblp.readthedocs.io/en/latest/>_.

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 <https://github.com/rstudio/reticulate>_ package makes interacting with PyBLP in R straightforward (when supported, Python objects can be converted to their R counterparts with the py_to_r function, which needs to be used manually because we set convert=FALSE to get rid of errors about trying to automatically convert unsupported objects)::

library(reticulate)
pyblp <- import("pyblp", convert=FALSE)
pyblp$options$flush_output <- TRUE

Similarly, PyCall <https://github.com/JuliaPy/PyCall.jl>_ can be used to incorporate PyBLP into a Julia workflow::

using PyCall
pyblp = pyimport("pyblp")

The py command <https://www.mathworks.com/help/matlab/call-python-libraries.html>_ serves a similar purpose in MATLAB::

py.pyblp

Features

R-style formula interface
Bertrand-Nash supply-side moments
Multiple equation GMM
Demographic interactions
Product-specific demographics
Consumer-specific product availability
Flexible micro moments that can match statistics based on survey data
Support for micro moments based on second choice data
Support for optimal micro moments that match micro data scores
Fixed effect absorption
Nonlinear functions of product characteristics
Concentrating out linear parameters
Flexible random coefficient distributions
Parameter bounds and constraints
Random coefficients nested logit (RCNL)
Approximation to the pure characteristics model
Varying nesting parameters across groups
Logit and nested logit benchmarks
Classic BLP instruments
Differentiation instruments
Optimal instruments
Covariance restrictions
Timing assumptions using moments based on unobservable innovations
Adjustments for simulation error
Tests of overidentifying and model restrictions
Parametric boostrapping post-estimation outputs
Elasticities and diversion ratios
Marginal costs and markups
Passthrough calculations
Profits and consumer surplus
Newton and fixed point methods for computing pricing equilibria
Merger simulation
Custom counterfactual simulation
Synthetic data construction
SciPy or Artleys Knitro optimization
Fixed point acceleration
Monte Carlo, quasi-random sequences, quadrature, and sparse grids
Importance sampling
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

Bugs and Requests

Please use the GitHub issue tracker <https://github.com/jeffgortmaker/pyblp/issues>_ to submit bugs or to request features.