Using stated response choice data to enrich revealed preference discrete choice models

1993 ◽  
Vol 4 (2) ◽  
pp. 139-151 ◽  
Author(s):  
David A. Hensher ◽  
Mark Bradley
Keyword(s):  
Discrete Choice ◽  
Revealed Preference ◽  
Choice Models ◽  
Discrete Choice Models ◽  
Choice Data ◽  
Response Choice ◽  
Stated Response

Benchmarks for Discrete Choice Models

1994 ◽  
Vol 31 (1) ◽  
pp. 65-75 ◽  
Author(s):  
Manohar U. Kalwani ◽  
Robert J. Meyer ◽  
Donald G. Morrison
Keyword(s):  
Discrete Choice ◽  
Consumer Choice ◽  
Repeated Measures ◽  
Choice Model ◽  
Simulated Data ◽  
Brand Choice ◽  
Choice Models ◽  
Discrete Choice Models ◽  
Choice Data ◽  
Choice Probabilities

In assessing the performance of a choice model, we have to answer the question, “Compared with what?” Analyses of consumer brand choice data historically have measured fit by comparing a model's performance with that of a naive model that assumes a household's choice probability on each occasion equals the aggregate market share of each brand. The authors suggest that this benchmark could form an overly naive point of reference in assessing the fit of a choice model calibrated on scanner-panel data, or any repeated-measures analysis of choice. They propose that fairer benchmarks for discrete choice models in marketing should incorporate heterogeneity in consumer choice probabilities, evidence for which is by now well documented in the marketing literature. They use simulated data to compare the performance of parametric and nonparametric benchmark models, which allow for heterogeneity in consumer choice probabilities, with the performance of the aggregate share-based benchmark model, which assumes consumers are homogeneous in their choice probabilities. They also assess the performance of two previously published consumer behavior models against the proposed fairer benchmark models that allow for heterogeneity in consumer choice probabilities. They find that one provides a significantly better fit than their more conservative benchmark models and the other performs less favorably.

Theoretical, methodological and practical issues of choice models

2018 ◽  
Vol 1 (1) ◽  
pp. 21-37
Author(s):  
Bharat P. Bhatta
Keyword(s):  
Discrete Choice ◽  
Model Building ◽  
Estimation Method ◽  
Choice Models ◽  
Discrete Choice Models ◽  
Decision Makers ◽  
Model Formulation ◽  
Random Utility Maximization ◽  
The Relationship ◽  
Method Model

This paper analyzes and synthesizes the fundamentals of discrete choice models. This paper alsodiscusses the basic concept and theory underlying the econometrics of discrete choice, specific choicemodels, estimation method, model building and tests, and applications of discrete choice models. Thiswork highlights the relationship between economic theory and discrete choice models: how economictheory contributes to choice modeling and vice versa. Keywords: Discrete choice models; Random utility maximization; Decision makers; Utility function;Model formulation

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