Lecture

Deterministic Part: Utility Function Coding

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Description

This lecture delves into the deterministic part of the utility function in the logit model for multiple alternatives. The instructor explains how to translate behavioral assumptions into mathematical expressions using variables representing alternative attributes and individual socio-economic characteristics. The lecture focuses on a linear-in-parameter specification of the utility function, where the function is a linear combination of these variables with unknown parameters to be estimated.

In MOOC

Introduction to Discrete Choice Models

Human behavior is complex, and unpredictable. Or is it? Predicting the behavior using mathematical models is the topic of this course. More specifically, we focus on choice modeling, in order to obtain disaggregate demand models. Focusing on the logit model, the course covers in details the specification of the model, the estimation of its parameters, the validation and the application. Exercises using the software Biogeme are also proposed. Concrete case studies with real data sets are provided.

Official source

Ontological neighbourhood

Statistics

Data analysis: Regression analysis

Related concepts (21)

Generalized linear model

In statistics, a generalized linear model (GLM) is a flexible generalization of ordinary linear regression. The GLM generalizes linear regression by allowing the linear model to be related to the response variable via a link function and by allowing the magnitude of the variance of each measurement to be a function of its predicted value. Generalized linear models were formulated by John Nelder and Robert Wedderburn as a way of unifying various other statistical models, including linear regression, logistic regression and Poisson regression.

Discrete choice

In economics, discrete choice models, or qualitative choice models, describe, explain, and predict choices between two or more discrete alternatives, such as entering or not entering the labor market, or choosing between modes of transport. Such choices contrast with standard consumption models in which the quantity of each good consumed is assumed to be a continuous variable. In the continuous case, calculus methods (e.g. first-order conditions) can be used to determine the optimum amount chosen, and demand can be modeled empirically using regression analysis.

Logit

In statistics, the logit (ˈloʊdʒɪt ) function is the quantile function associated with the standard logistic distribution. It has many uses in data analysis and machine learning, especially in data transformations. Mathematically, the logit is the inverse of the standard logistic function , so the logit is defined as Because of this, the logit is also called the log-odds since it is equal to the logarithm of the odds where p is a probability. Thus, the logit is a type of function that maps probability values from to real numbers in , akin to the probit function.

Logistic regression

In statistics, the logistic model (or logit model) is a statistical model that models the probability of an event taking place by having the log-odds for the event be a linear combination of one or more independent variables. In regression analysis, logistic regression (or logit regression) is estimating the parameters of a logistic model (the coefficients in the linear combination).

Axiom of choice

In mathematics, the axiom of choice, abbreviated AC or AoC, is an axiom of set theory equivalent to the statement that a Cartesian product of a collection of non-empty sets is non-empty. Informally put, the axiom of choice says that given any collection of sets, each containing at least one element, it is possible to construct a new set by arbitrarily choosing one element from each set, even if the collection is infinite. Formally, it states that for every indexed family of nonempty sets, there exists an indexed set such that for every .

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