Discrete Choice Analysis: Predicting Individual Behavior | Ecublens

Discrete Choice Analysis: Predicting Individual Behavior

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Accurate predictions of the demand and market shares are critical for a wide variety of businesses and public organizations. Examples of applications include: predicting demand for a new product under alternative pricing strategies; designing a business plan for a new technology; analyzing the impact of a merger on market shares; forecasting the ridership on a new metropolitan transit service; and analyzing competitive scenarios for introducing a new telecommunication service. To accomplish these tasks, discrete choice analysis provides powerful methodological tools. Based on the modeling of individual behavior, it is used to model in detail the structure of a market, and to predict the impact of various scenarios.
This one-week program undertakes an in-depth study of discrete choice models and their applications. It provides participants with the practical tools necessary for applying new discrete choice techniques. By examining actual case studies of discrete choice methods students will be familiarized with problems of data collection, model formulation, testing, and forecasting and will gain hands-on application experience by using readily available software to estimate and test discrete choice models from real databases. The course will emphasize applications of discrete choice methods to strategic and tactical marketing and to policy-related problems.
The course will cover the following topics:
Fundamental methodology, e.g. the foundations of individual choice modeling, random utility models, discrete choice models (binary, multinomial, nested, cross-nested logit models, MEV models, probit models, and hybrid choice models such as logit kernel and mixed logit);
Data collection issues, e.g. choice-based samples, enriched samples, stated preferences surveys, conjoint analysis, panel data;
Model design issues, e.g. specification of utility functions, generic and alternative specific variables, joint discrete/continuous models, dynamic choice models;
Model estimation issues, e.g. statistical estimation, testing procedures, software packages, estimation with individual and grouped data, Bayesian estimation;
Forecasting techniques, e.g. aggregate predictions, sample enumeration, micro-simulation, elasticities, pivot-point predictions and transferability of parameters;
Examples and case studies, including marketing (e.g., brand choice), housing (e.g., residential location), telecommunications (e.g., choice of residential telephone service), energy (e.g., appliance type), transportation (e.g., mode of travel).


09:00 Uhr, Sonntag, 24. März 2019


Ecole polytechnique fédérale de Lausanne (EPFL)
Route des Noyerettes 1015 Ecublens Switzerland

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Transport and Mobility Laboratory, Ecublens

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