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Lecture
The red bud-blue bus paradox
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Related lectures (31)
Red bus/Blue bus paradox
Explores the Red bus/Blue bus paradox, nested logit models, and multivariate extreme value models in transportation.
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.
Discrete Choice Analysis
Introduces Discrete Choice Analysis, covering scale, depth, data collection, and statistical inference.
Mixtures: introduction
MOOC: Selected Topics on Discrete Choice
Introduces mixtures, covers discrete and continuous mixtures, explores examples, and discusses combining probit and logit 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.
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.
Logistic Regression: Modeling Binary Response Variables
Explores logistic regression for binary response variables, covering topics such as odds ratio interpretation and model fitting.
Panel data: static model
MOOC: Selected Topics on Discrete Choice
Introduces the static model for panel data analysis and discusses its limitations.
Generalized Linear Regression: Classification
Explores Generalized Linear Regression, Classification, confusion matrices, ROC curves, and noise in data.
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: summary
MOOC: Selected Topics on Discrete Choice
Summarizes mixtures of logit models, covering various mixing methods and modeling techniques for taste heterogeneity.
Deterministic Part: Utility Function Coding
MOOC: Introduction to Discrete Choice Models
Explores coding behavioral assumptions into a linear-in-parameter utility function.
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.
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.
Horseshoe Crabs: Logistic Regression Analysis
Explores logistic regression analysis of horseshoe crab data, focusing on odds ratio interpretation and model fitting.
Revenue Maximization: Introduction to Choice Models
Covers revenue maximization in choice models, pricing strategies, market competition, and a binary logit model example.
Extreme Value Models: Technical Derivation
MOOC: Selected Topics on Discrete Choice
Explores the technical derivation and properties of Multivariate Extreme Value models.
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