Mohit Batham, Soudeh Mirghasemi, Mohammad Arshad Rahman, Manini Ojha
arXiv 21 Sep 2021 · Econometrics
arXiv:2109.10122 · PDF · DOI · OpenAlex · Extracted main text
This chapter presents an overview of a specific form of limited dependent variable models, namely discrete choice models, where the dependent (response or outcome) variable takes values which are discrete, inherently ordered, and characterized by an underlying continuous latent variable. Within this setting, the dependent variable may take only two discrete values (such as 0 and 1) giving rise to binary models (e.g., probit and logit models) or more than two values (say $j=1,2, \ldots, J$, where $J$ is some integer, typically small) giving rise to ordinal models (e.g., ordinal probit and ordinal logit models). In these models, the primary goal is to model the probability of responses/outcomes conditional on the covariates. We connect the outcomes of a discrete choice model to the random utility framework in economics, discuss estimation techniques, present the calculation of covariate effects and measures to assess model fitting. Some recent advances in discrete data modeling are also discussed. Following the theoretical review, we utilize the binary and ordinal models to analyze public opinion on marijuana legalization and the extent of legalization -- a socially relevant but controversial topic in the United States. We obtain several interesting results including that past use of marijuana, belief about legalization and political partisanship are important factors that shape the public opinion.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Johnson, V. E. and Albert, J. H (2000) Ordinal Data Modeling | 0.928 | 4 | 4 | 100% |
| 2 | Jeliazkov, I. and Rahman, M. A (2012) Binary and Ordinal Data Analysis in Economics: Modeling and Estimation, in self | 0.928 | 4 | 3 | 100% |
| 3 | Nielsen, A. L (2007) Americans' Attitudes toward Drug-Related Issues from 1975-2006: The Roles of Period and Cohort Effects | 0.644 | 4 | 1 | 100% |
| 4 | Jeliazkov, I., Graves, J., and Kutzbach, M (2008) Fitting and Comparison of Models for Multivariate Ordinal Outcomes | 0.644 | 2 | 2 | 100% |
| 5 | Train, K (2009) Discrete Choice Methods with Simulation | 0.644 | 2 | 2 | 100% |
| 6 | Saieva, A. P (2008) Marijuana Legalization: American's Attitudes over Four Decades, M.S | 0.585 | 3 | 1 | 100% |
| 7 | Delforterie, M. J., Cremers, H. E., Agrawal, A., Lynskey, M. T., Jak… (2015) The Influence of Age and Gender on the Likelihood of Endorsing Cannabis Abuse/Dependence Criteria | 0.511 | 2 | 1 | 100% |
| 8 | Greenberg, E (2012) Introduction to Bayesian Econometrics | 0.511 | 2 | 1 | 100% |
| 9 | Greene, W. H. and Hensher, D. A (2010) Modeling Ordered Choices: A Primer | 0.511 | 2 | 1 | 100% |
| 10 | McFadden, D (1974) Conditional Logit Analysis of Qualitative Choice Behavior, in | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 64 scored citations.