The red bud-blue bus paradox

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Related lectures (31)

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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.

Generalized Linear Models: A Brief Review

Provides an overview of Generalized Linear Models, focusing on logistic and Poisson regression models, and their implementation in R.

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

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.

Maximum Likelihood Theory & Applications

Covers maximum likelihood theory, applications, and hypothesis testing principles in econometrics.

Derivation of the logit model

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

Mixture models: summary

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

Linear Models for Classification: Logistic Regression and SVM

Covers linear models for classification, focusing on logistic regression and support vector machines.

Deterministic Part: Utility Function Coding

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