Choice Models: Logit Model Derivation

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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.

Mixture Models: Simulation-based Estimation

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

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.

Discrete Choice Analysis

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

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

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

Mixtures: introduction MOOC: Selected Topics on Discrete Choice

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

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.

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.

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.

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

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

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.

Revenue Maximization: Introduction to Choice Models

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

Model Specification: The Error Term MOOC: Introduction to Discrete Choice Models

Delves into the binary choice model, error term specification, and Extreme Value distribution properties.

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.

Mixture models: summary MOOC: Selected Topics on Discrete Choice

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

Binary Choice Model

Covers the binary choice model, error term assumptions, specific constants, invariances, and distribution properties.

Weighted Least Squares Estimation: IRLS Algorithm

Explores the IRLS algorithm for weighted least squares estimation in GLM.

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.

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