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A Distinguished Speaker Series Seminar by William H. Greene Ph.D.

Many applications in the social sciences are about why people make various choices among a discrete set of options. Why do voters choose one candidate over another? Why do high school students choose one college over others that have accepted them? Why do consumers choose one brand of toothpaste among many options to choose from?

This seminar is an introduction to econometric models for such discrete choices. Data for these kinds of models are typically measured at the level of the individual decision maker. Response data include identifiers of qualitative outcomes such as opinions, political positions, or self-assessments, as well as choices among two or more alternatives such as travel mode, brand choice, location choice, or activities. Predictors generally include both characteristics of the choosers, such as age, income, education, and gender, and attributes of the available options, such as prices, travel time, proximity, familiarity, etc.

We will discuss several types of discrete choice models for describing individual choice behavior. These techniques are natural platforms for both observational and experimental data. Models will build on the canonical binary choice framework to the two most common multiple-choice settings: ordered choices such as attitude and opinion scales and unordered choices such as brand, label, type, or location choices.

Although the course is not hands on, many applications will be discussed. One application is an analysis of survey data on the adoption of green technology and whether that decision appears to be influenced by knowledge of widely-discussed efforts to influence public opinion. The techniques are available in widely-used commercial software such as Stata, SAS, and NLOGIT. They are also provided by user-written code in R, Gauss, MatLab, etc.

The course is intended for researchers who wish to develop and use statistical models of individual choices. The presentation will be at the level of an intermediate graduate course in econometrics or statistics. Participants should have had an econometrics/statistics course that progressed beyond the linear model, say, to the basic probit and logit models for binary choice. We will build outward from there.