Lecture

Choice models with latent variables: Modeling latent concepts

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Description

This lecture covers choice models with latent variables, focusing on modeling latent concepts using structural equations for utility and indicators, as well as measurement equations for continuous and discrete indicators. The estimation process involves likelihood integration and maximum likelihood estimation. The content is based on the work of Walker (2001).

In MOOC

Selected Topics on Discrete Choice

The logit model is the workhorse of choice modelers. But it has some limitations. In particular, some assumptions used to derive it may not be consistent with the behavioral reality. It may lead to erroneous forecast. We illustrated using the so-called "red bud-blue bus" paradox, and Multivariate Extre Value models, addressing some of these issues, are introduced. The sampling procedure used to collect choice data has a critical impact on the model estimation procedure. We introduce classical sampling proc

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Related concepts (32)

Structural equation modeling

Structural equation modeling (SEM) is a diverse set of methods used by scientists doing both observational and experimental research. SEM is used mostly in the social and behavioral sciences but it is also used in epidemiology, business, and other fields. A definition of SEM is difficult without reference to technical language, but a good starting place is the name itself. SEM involves a model representing how various aspects of some phenomenon are thought to causally connect to one another.

Latent and observable variables

In statistics, latent variables (from Latin: present participle of lateo, “lie hidden”) are variables that can only be inferred indirectly through a mathematical model from other observable variables that can be directly observed or measured. Such latent variable models are used in many disciplines, including political science, demography, engineering, medicine, ecology, physics, machine learning/artificial intelligence, bioinformatics, chemometrics, natural language processing, management, psychology and the social sciences.

Latent variable model

A latent variable model is a statistical model that relates a set of observable variables (also called manifest variables or indicators) to a set of latent variables. It is assumed that the responses on the indicators or manifest variables are the result of an individual's position on the latent variable(s), and that the manifest variables have nothing in common after controlling for the latent variable (local independence).

Maximum likelihood estimation

In statistics, maximum likelihood estimation (MLE) is a method of estimating the parameters of an assumed probability distribution, given some observed data. This is achieved by maximizing a likelihood function so that, under the assumed statistical model, the observed data is most probable. The point in the parameter space that maximizes the likelihood function is called the maximum likelihood estimate. The logic of maximum likelihood is both intuitive and flexible, and as such the method has become a dominant means of statistical inference.

Rational choice theory

Rational choice theory refers to a set of guidelines that help understand economic and social behaviour. The theory originated in the eighteenth century and can be traced back to political economist and philosopher, Adam Smith. The theory postulates that an individual will perform a cost-benefit analysis to determine whether an option is right for them. It also suggests that an individual's self-driven rational actions will help better the overall economy. Rational choice theory looks at three concepts: rational actors, self interest and the invisible hand.

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