PPT-Discrete Choice Modeling

William Greene Stern School of Business New York University Lab Sessions Data Sets for Random Parameters Modeling 1 clogitlpj as before 2 brandchoicesSPLPJ is 8 choice situations per person 4 choices True underlying model is a three class latent class model. William Greene. Stern School of Business. New York University. Part 7-1. Latent Class Models. Discrete Parameter Heterogeneity. Latent Classes. Latent Class Probabilities. Ambiguous – Classical Bayesian model?. William Greene. Stern School of Business. New York University. 0 Introduction. 1 . Summary. 2 Binary Choice. 3 Panel Data. 4 Bivariate Probit. 5 Ordered Choice. 6 Count Data. 7 Multinomial Choice. 8 Nested Logit. William Greene. Stern School of Business. New York University. Part 5. Multinomial Logit Extensions. What’s Wrong with the MNL Model?. I. .I.D. .  IIA . Independence from irrelevant alternatives. University of Texas at Austin. Chandra R. . Bhat. Introduction: . Choice Modeling. A set of tools to predict the choice behavior of a group of decision-makers in a specific choice context.. Picture Reference: Future and Simple-Choice Modeling (by Steve Cook and Michael McGee). William Greene. Stern School of Business. New York University. 0 Introduction. 1 . Summary. 2 Binary Choice. 3 Panel Data. 4 Bivariate Probit. 5 Ordered Choice. 6 Count Data. 7 Multinomial Choice. 8 Nested Logit. Dr. Feng Gu. Way to study a system. . Cited from Simulation, Modeling & Analysis (3/e) by Law and . Kelton. , 2000, p. 4, Figure 1.1. Model taxonomy. Modeling formalisms and their simulators . Discrete time model and their simulators . William Greene. Stern School of Business. New York University. Part 6. Modeling Heterogeneity. Several Types of Heterogeneity. Differences across choice makers. Observable: Usually demographics such as age, sex. William Greene. Stern School of Business. New York University. Lab Sessions. Lab Session 2. Analyzing Binary Choice Data. Data Set: Load PANELPROBIT.LPJ. Fit Basic Models. . Partial Effects for Interactions. William Greene. Stern School of Business. New York University. Part 5.2. The Nested Logit Model. Extended Formulation of the MNL. . Clusters of similar alternatives. . Compound Utility: U(Alt)=U(Alt|Branch)+U(branch). Ben Valentino. 1. , Eduardo Toledo. 2. , Eduardo Nobre. 2. , Luciana Vieira. 2. , . Diogo. Cintura. 2. 1. Department of Earth Sciences, SUNY Oswego. 2. Department of Civil Engineering, . Federal University of . Cornell University. School of ORIE. Jake Feldman . Cornell University. School of ORIE. Upper Bounds on the Optimal Expected Revenue for Assortment Optimization under the Mixed Multinomal Logit Choice Model. William Greene. Stern School of Business. New York University. 0 Introduction. 1 . Summary. 2 Binary Choice. 3 Panel Data. 4 Bivariate Probit. 5 Ordered Choice. 6 Count Data. 7 Multinomial Choice. 8 Nested Logit. Discrete Choice Modeling William Greene Stern School of Business New York University Part 2 Estimating and Using Binary Choice Models Agenda A Basic Model for Binary Choice Specification Maximum Likelihood Estimation Yuanyuan . Gu, PhD. Senior Research Fellow. CENTRE FOR THE HEALTH ECONOMY. Co . authors:. Henry Cutler, PhD. Director. Emma Olin. Research Fellow. AHES Conference 2017. Introduction. Background and study objectives.