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- Date
March 15, 2015
- Version
1.0.0
- Authors
Quentin ANDRE, quentin.andre@insead.edu
- Web site
- Copyright
This document has been placed in the public domain.
- License
Guadagni-Little-1983-Python is released under the MIT license.
Guadagni-Little-1983-Python provides a Python implementation of the mixed/conditional logit model of product choice outlined by Guadagni and Little in their 1983 article "A Logit Model of Brand Choice Calibrated on Scanner Data".
- 1. Dataset Recreation.py: A script to create from scratch a purchase history dataset of N consumers over T time periods for K options. All the parameters in the script can be changed to generate a different dataset. As in the original paper, the consumers are heterogenous in their loyalty to the brand and to the different sizes offered. The script outputs four files:
- GuadagniLittle1983.csv, which contains the simulated scanner data
- TrueBetas.csv, which contains the true parameters used to generate the data.
- BrandShares.png, which plots the evolution of market shares for the brands over time.
- SizeShares.png, which plots the evolution of market shares for the sizes over time.
- Mixed Logit Estimation.py: A script to recover the parameters used to generate the data. As in the original paper, the the utility of the first option is constrained to be 1 to allow identification of the (K-1) brand intercepts and of the J utility components for the attributes (which are common to all brands).
- Recreation and Estimation.ipynb: An IPython notebook combining the two aforementioned files, and describing in details the different steps of the data creation and data estimation process
- Recreation and Estimation.html: An HTML export of the aforementioned notebook.
- This code has been tested in Python 3.4, using the Anaconda distribution:
- * Using git:
- * Download the master branch as a zip:
Guadagni, P. M., & Little, J. D. (1983). A logit model of brand choice calibrated on scanner data. Marketing science, 2(3), 203-238.