Introduction

Purpose

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".

Content

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.

2. 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).
3. 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
4. Recreation and Estimation.html: An HTML export of the aforementioned notebook.

Installation

Dependencies

This code has been tested in Python 3.4, using the Anaconda distribution:

• The Anaconda distribution for Python 3.4

Download

• Using git:

• git clone https://github.com/QuentinAndre/Guadagni-Little-1983-Python.git

• Download the master branch as a zip:

• https://github.com/QuentinAndre/Guadagni-Little-1983-Python/archive/master.zip

References

Guadagni, P. M., & Little, J. D. (1983). A logit model of brand choice calibrated on scanner data. Marketing science, 2(3), 203-238.