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https://github.com/jeffgortmaker/pyblp

BLP Demand Estimation with Python
https://github.com/jeffgortmaker/pyblp

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BLP Demand Estimation with Python

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README 

        
PyBLP

=====



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.. description-start



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



.. 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 `_ 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 (2020) `_, 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) `_, 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 `_ versions 3.6 through 3.9. 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 it comes packaged with the following PyBLP dependencies: `NumPy `_, `SciPy `_, `SymPy `_, and `Patsy `_. For absorption of high dimension fixed effects, PyBLP also depends on its companion package `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 `_::



    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 (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 `_ 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

- 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

- 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 `_ to submit bugs or to request features.