Hostname: page-component-76fb5796d-wq484 Total loading time: 0 Render date: 2024-04-26T04:55:21.240Z Has data issue: false hasContentIssue false
  • English
  • Français

Modeling-Related Processes With an Excess of Zeros

Published online by Cambridge University Press:  09 July 2018

David Carlson
Get access Rights & Permissions [Opens in a new window]

Abstract

Political science research frequently models binary or ordered outcomes involving related processes. However, traditional modeling of these outcomes ignores common data issues and cannot capture nuances. There is often an excess of zeros, the observed outcomes for different actors are inherently related, and competing actors may respond to the same factors differently. This paper extends existing models and develops a zero-inflated multivariate ordered probit to simultaneously address these issues. This model performs better than existing models at capturing the true parameters of interest, estimates the nature of the related processes, and captures the differences in actors’ decision-making. I demonstrate these benefits through simulation exercises and an application to party behavior in Mexico.

Type
Research Note
Information
Political Science Research and Methods , Volume 7 , Issue 4 , October 2019 , pp. 889 - 902
Copyright
© The European Political Science Association 2018 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

*

Department of Political Science, Washington University in Saint Louis, Campus Box 1063, 1 Brookings Drive, Saint Louis, MI 63130-4899 (carlson.david@wustl.edu). The author would like to sincerely thank Siddhartha Chib, Jeff Gill, Jacob M. Montgomery, Guillermo Rosas, Margit Tavits, Michelle Torres, every member of the Comparative Politics Workshop and of the Data Science Lab of Washington University in St. Louis for their invaluable comments. To view supplementary material for this article, please visit https://doi.org/10.1017/psrm.2018.25

References

Albert, James H., and Chib, Siddhartha. 1993. ‘Bayesian Analysis of Binary and Polychotomous Response Data’. Journal of the American Statistical Association 88(422):669–679.Google Scholar
Bagozzi, Benjamin E., Hill, Daniel W., Moore, Will H., and Mukherjee, Bumba. 2015. ‘Modeling Two Types of Peace: The Zero-Inflated Ordered Probit (Ziop) Model in Conflict Research’. Journal of Conflict Resolution 59(4):728–752.Google Scholar
Bagozzi, Benjamin E., and Marchetti, Kathleen. 2014. Distinguishing Occasional Abstention from Routine Indifference in Models of Vote Choice. Political Science Research and Methods 5(2):277–294.Google Scholar
Cameron, Colin, and Trivedi, Pravin K.. 2005. Microeconometrics: Methods and Applications. New York, NY: Cambridge University Press.Google Scholar
Dunne, John Paul, and Tian, Nan. n.d. The Determinants of Civil War and Excess Zeros. Working Paper, University of Cape Town, Cape Town.Google Scholar
Gurmu, Shifrew, and Dagne, Gatachew A.. 2012. Bayesian Approach to Zero-Inflated Bivariate Ordered Probit Regression Model, With an Application to Tobacco Use. Journal of Probability and Statistics 2012:26, Article ID 617678.Google Scholar
Harris, Mark N., and Zhao, Xueyan. 2007. ‘A Zero-Inflated Ordered Probit Model, With an Application to Modelling Tobacco Consumption’. Journal of Econometrics 141(2):1073–1099.Google Scholar
Kadel, Rajendra. 2013. ‘A Latent Mixture Approach to Modeling Zero-Inflated Bivariate Ordinal Data’. PhD Thesis, University of South Florida, Tampa, FL.Google Scholar
King, Gary. 1989. ‘A Seemingly Unrelated Poisson Regression Model’. Sociological Methods & Research 17(3):235–255.Google Scholar
King, Gary, and Zeng, Langche. 2001a. ‘Explaining Rare Events in International Relations’. International Organization 55(3):693–715.Google Scholar
King, Gary, and Zeng, Langche. 2001b. ‘Logistic Regression in Rare Events Data’. Political Analysis 9(2):137–163.Google Scholar
Langston, Joy, and Rosas, Guillermo. 2016. Presidential Campaigns Under Single-District Plurality: Visits, Rallies, and the Calculus of Electoral Mobilization in Mexico. Submitted.Google Scholar
Laslier, Jean-F., and Picard, Nathalie. 2002. ‘Distributive Politics and Electoral Competition’. Journal of Economic Theory 103(1):106–130.Google Scholar
Nieman, M. D. 2015. Statistical Analysis of Strategic Interaction With Unobserved Player Actions: Introducing a Strategic Probit With Partial Observability. Political Analysis 23(3):429–48.Google Scholar
Zellner, Arnold 1962. ‘An Efficient Method of Estimating Seemingly Unrelated Regressions and Tests for Aggregation Bias’. Journal of the American statistical Association 57(298):348–368.Google Scholar
Zellner, Arnold, and Huang, David S. 1962. ‘Further Properties of Efficient Estimators for Seemingly Unrelated Regression Equations’. International Economic Review 3(3):300–313.Google Scholar
Supplementary material: PDF

Carlson supplementary material

Carlson supplementary material 1

Download Carlson supplementary material(PDF)
PDF 90.7 KB
Supplementary material: Link

Carlson Dataset

Dataset

https://doi.org/10.7910/DVN/ZWEA8L
Link