Concept

Logit

Related lectures (31)

Mixture Models: Simulation-based Estimation

Explores mixture models, including discrete and continuous mixtures, and their application in capturing taste heterogeneity in populations.

The Nested Logit Model MOOC: Selected Topics on Discrete Choice

Explores the nested logit model for discrete choice and its implications on choice behavior and parameter estimation.

Mixture models: alternative specific variance MOOC: Selected Topics on Discrete Choice

Explores alternative specific variance in mixture models and discusses identification issues and model comparisons using 500 draws.

Mixtures: introduction MOOC: Selected Topics on Discrete Choice

Introduces mixtures, covers discrete and continuous mixtures, explores examples, and discusses combining probit and logit models.

Extreme Value Models: Technical Derivation MOOC: Selected Topics on Discrete Choice

Explores the technical derivation and properties of Multivariate Extreme Value models.

Sampling: conditional maximum likelihood estimation MOOC: Selected Topics on Discrete Choice

Covers Conditional Maximum Likelihood estimation, contribution to likelihood, and MEV model application in choice-based samples.

Discrete Choice Analysis

Introduces Discrete Choice Analysis, covering scale, depth, data collection, and statistical inference.

Panel data: static model MOOC: Selected Topics on Discrete Choice

Introduces the static model for panel data analysis and discusses its limitations.

Mixture models: summary MOOC: Selected Topics on Discrete Choice

Summarizes mixtures of logit models, covering various mixing methods and modeling techniques for taste heterogeneity.

The red bud-blue bus paradox MOOC: Selected Topics on Discrete Choice

Discusses the red bud-blue bus paradox in mode choice modeling and its policy implications.

Logistic Regression: Modeling Binary Response Variables

Explores logistic regression for binary response variables, covering topics such as odds ratio interpretation and model fitting.

Logistic Regression: Part 1

Introduces logistic regression for binary classification and explores multiclass classification using OvA and OvO strategies.

Derivation of the logit model MOOC: Introduction to Discrete Choice Models

Delves into the logit model's derivation, emphasizing the importance of the independence assumption and parameter normalization during estimation.

Deterministic Part: Utility Function Coding MOOC: Introduction to Discrete Choice Models

Explores coding behavioral assumptions into a linear-in-parameter utility function.

Bayesian Estimation: Overview and Examples

Introduces Bayesian estimation, covering classical versus Bayesian inference, conjugate priors, MCMC methods, and practical examples like temperature estimation and choice modeling.

Red bus/Blue bus paradox

Explores the Red bus/Blue bus paradox, nested logit models, and multivariate extreme value models in transportation.

Derivation of the logit model MOOC: Introduction to Discrete Choice Models

Explains the derivation of the logit model in choice models, covering error terms, choice sets, and availability conditions.

Discrete Choice Models: Selected Topics MOOC: Selected Topics on Discrete Choice

Covers theoretical foundations and methodologies of discrete choice models for predicting behavior and obtaining demand models.

Revenue Maximization: Introduction to Choice Models

Covers revenue maximization in choice models, pricing strategies, market competition, and a binary logit model example.

Choice Models: Logit Model Derivation

Covers the derivation of the logit model for choice with multiple alternatives and the challenges in generating choice sets.