Lectures related to Panel data: static model

Mixture Models: Simulation-based Estimation

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

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.

Red bus/Blue bus paradox

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

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.

Logistic Regression: Modeling Binary Response Variables

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

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.

Horseshoe Crabs: Logistic Regression Analysis

Explores logistic regression analysis of horseshoe crab data, focusing on odds ratio interpretation and model fitting.

Mixtures: introduction MOOC: Selected Topics on Discrete Choice

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

Generalized Linear Regression: Classification

Explores Generalized Linear Regression, Classification, confusion matrices, ROC curves, and noise in data.

Logistic Regression: Statistical Inference and Machine Learning

Covers logistic regression, likelihood function, Newton's method, and classification error estimation.

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.

Logistic Regression: Model Interpretation and Comparison

Explores logistic regression model interpretation, parameter estimation, and model comparison using likelihood ratio tests.

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.

Mixture models: summary MOOC: Selected Topics on Discrete Choice

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

Generalized Linear Models: Theory and Applications

Covers the theory and applications of Generalized Linear Models, including MLE, measures of fit, shrinkage, and special examples.

Discrete Panel Data

Explores discrete panel data, covering static and dynamic models with panel effects and their practical implications.

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

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

Generalized Linear Models: GLMs for Non-Gaussian Data

Explores Generalized Linear Models for non-Gaussian data, covering interpretation of natural link function, MLE asymptotic normality, deviance measures, residuals, and logistic regression.