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jeffgortmaker / pyblp

BLP Demand Estimation with Python
https://pyblp.readthedocs.io
MIT License
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PyBLP

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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/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 = {The RAND Journal of Economics},
    volume = {51},
    number = {4},
    pages = {1108-1161},
    doi = {https://doi.org/10.1111/1756-2171.12352},
    url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/1756-2171.12352},
    eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/1756-2171.12352},
    year = {2020}
}

If you use PyBLP's micro moments functionality, we ask that you also cite Conlon and Gortmaker (2023) <https://jeffgortmaker.com/files/micro.pdf>_, which describes the standardized framework implemented by PyBLP for incorporating micro data into BLP-style estimation. ::

@misc{MicroPyBLP,
    author = {Conlon, Christopher and Gortmaker, Jeff},
    title = {Incorporating micro data into differentiated products demand estimation with {PyBLP}},
    note = {Working paper},
    year = {2023}
}

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

Bugs and Requests

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