Date of Award

Summer 8-2018

Author's School

Graduate School of Arts and Sciences

Author's Department

Statistics

Additional Affiliations

Political Science

Degree Name

Master of Arts (AM/MA)

Degree Type

Thesis

Abstract

Multinomial logistic regression model (MNL) is a powerful and easily tractable way for measuring the probabilistic impact of input variables on individual categorical choices. Crucially, the standard MNL assumes that all subjects of the study have the same choice sets. In the meanwhile, especially in political science and economics, this condition is frequently violated. Probably, the most graphical example of varying choice sets (VCS) is partially contested elections. Furthermore, the MNL implicitly implies the Independence of the Irregular Alternatives (IIA) assumption by requiring i.i.d errors that contrasts the MNL and the multinomial probit (MNP) and mixed logit (MXL) models. In the case of VCS in the MNL, the errors are correlated and IIA is clearly violated. However, neither MNP nor MXL allows estimating particular parameters for distinct choice sets. This obstacle is critical if the aim is to compare the selection process conditional on the choice restrictions. This text argues that the MNL proposes the best opportunity to model categorical choice given VCS. For that, it advances the theory of MNL adjusting this classical model for the case of VCS. Second, the paper proposes a way to calculate and evaluate the model posing minimal data restrictions. Finally, this research provides an example of the model's application.

Language

English (en)

Chair and Committee

Betsy Sinclair

Committee Members

José E. Figueroa-López

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