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Activity Number: 354
Type: Contributed
Date/Time: Tuesday, August 2, 2016 : 10:30 AM to 12:20 PM
Sponsor: Business and Economic Statistics Section
Abstract #321237 View Presentation
Title: A Generalized Ordered Response Model
Author(s): Kramer Quist* and James McDonald and Carla Johnston
Companies: Brigham Young University and Brigham Young University and University of California at Berkeley
Keywords: Semiparametric ; SGT ; Categorical Data ; Big Data ; Monte Carlo Simulations

The ordered probit and logit models, based on the normal and logistic distributions, respectively can yield biased and inconsistent estimators when the distributions are misspecified. A generalized ordered response model is introduced which can reduce the impact of distributional misspecification. An empirical exploration of various determinants of life satisfaction demonstrates the benefits of allowing for diverse distributional characteristics. We experiment with Monte Carlo estimation techniques to analyze how generalized ordered response model's compare to probit and logit models in various sample sizes.

Authors who are presenting talks have a * after their name.

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