Lectures in MOOC Selected Topics on Discrete Choice

Discrete choice and machine learning: two methodologies MOOC: Selected Topics on Discrete Choice

Delves into the complementary methodologies of discrete choice and machine learning, covering notations, variables, models, data processes, extrapolation, what-if analysis, and more.

Sampling strategies MOOC: Selected Topics on Discrete Choice

Explores research process, variable types, causality vs correlation, and sampling strategies.

Latent variables in choice models: Optima case study MOOC: Selected Topics on Discrete Choice

Explores the Optima case study, analyzing attitudes and mode choice.

Beyond Rationality: Behavioral Economics MOOC: Selected Topics on Discrete Choice

Delves into human decision-making beyond rationality, exploring concepts like the decoy effect.

Mixture models: nesting structures MOOC: Selected Topics on Discrete Choice

Covers mixture models, nesting structures, and identification issues in discrete choice analysis.

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.

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.

Monte-Carlo Integration MOOC: Selected Topics on Discrete Choice

Covers Monte-Carlo integration, simulation, and transforming draws from different distributions.

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

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

Panel data: serial correlation MOOC: Selected Topics on Discrete Choice

Explores the panel effect model with fixed and random effects, discussing estimation challenges and the impact of serial correlation.

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.

Monte-Carlo Integration: Approximation and Variance MOOC: Selected Topics on Discrete Choice

Explores Monte-Carlo integration for approximating expectations and variances using random sampling and discusses error components in conditional choice models.

Mixture models: taste heterogeneity MOOC: Selected Topics on Discrete Choice

Explores mixture models in discrete choice and random parameters estimation results.

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

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

Choice models with latent variables: Modeling latent concepts MOOC: Selected Topics on Discrete Choice

Explores choice models with latent variables and their estimation process based on likelihood integration.

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 and machine learning: two complementary methodologies MOOC: Selected Topics on Discrete Choice

Explores discrete choice and machine learning as complementary methodologies, discussing supervised learning, model advantages, pitfalls, aggregation bias, probabilistic classification, and panel data.

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

Explores sampling in maximum likelihood estimation and its implications on the joint probability and likelihood contribution.

Panel data: dynamic model with panel effects MOOC: Selected Topics on Discrete Choice

Covers the Dynamic Markov model with panel effects, addressing initial condition and endogeneity.

Choice models with latent variables: Modeling latent concepts MOOC: Selected Topics on Discrete Choice

Explores choice models with latent variables, including indirect measurements and psychometric indicators.