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Discrete Choice Modeling

William Greene Stern School of Business New York University. Discrete Choice Modeling. 0 Introduction 1 Methodology 2 Binary Choice 3 Panel Data 4 Bivariate Probit 5 Ordered Choice 6 Count Data 7 Multinomial Choice 8 Nested Logit 9 Heterogeneity 10 Latent Class 11 Mixed Logit

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Discrete Choice Modeling

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  1. William Greene Stern School of Business New York University Discrete Choice Modeling 0 Introduction 1 Methodology 2 Binary Choice 3 Panel Data 4 Bivariate Probit 5 Ordered Choice 6 Count Data 7 Multinomial Choice 8 Nested Logit 9 Heterogeneity 10 Latent Class 11 Mixed Logit 12 Stated Preference 13 Hybrid Choice 14 Spatial Data

  2. Discrete Choice Modeling • Theoretical Foundations • Econometric Methodology • Regression Analysis • Binary Choice Models • Discrete Choice Models • Multinomial Choice Models • Model Building, Econometric Methods • Statistical Bases, Specification Issues • Estimation, Coefficients, Partial Effects • Analysis • Inference and Hypothesis Testing • Applications

  3. Agenda Day 1: Foundations 0 Introduction 1Methodology: Basic Regression Modeling 2 Binary Choice: Estimation, Inference, Analysis, Partial Effects 3 Panel Data: Fixed and Random Effects 4 Bivariate Probit: Bivariate, Recursive and Multivariate Models Day 2: Model Extensions; Multinomial Choice 5 Ordered Choice: Ordered Probit, Vignettes 6 Count Data: Poisson, Negative Binomial, Two Part Models 7 Multinomial Choice: The Multinomial Logit Model 8 Nested Logit: Nesting, Multinomial Probit Model Day 3: Multinomial Choice, Advanced Models, Stated Choice 9Heterogeneity: Scale Effects, WTP, RP and LC 10 Latent Class: Finite Mixtures, Attribute Nonattendance 11 Mixed Logit: Random Parameter Models 12 Stated Preference: Choice Experiments, Best/Worst Data 13 Hybrid Choice: Mixed Discrete and Continuous Choice 14 (Time permitting) Discrete Choice Models for Spatial Data

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