Zero-Inflated Generalized Linear Mixed Models: A Better Way to Understand Data Relationships

Luiz Paulo Fávero, Joseph F. Hair, Rafael de Freitas Souza, Matheus Albergaria and Talles V. Brugni
Additional contact information
Luiz Paulo Fávero: Business and Management Department, School of Economics, Business and Accounting, University of São Paulo—USP, São Paulo 05508-010, Brazil
Joseph F. Hair: Marketing & Quantitative Methods, Mitchell College of Business, University of South Alabama, Mobile, AL 36688, USA
Rafael de Freitas Souza: Business and Management Department, School of Economics, Business and Accounting, University of São Paulo—USP, São Paulo 05508-010, Brazil
Matheus Albergaria: Postgraduate Department of Business Management and Accounting, Fundação Escola de Comércio Álvares Penteado—FECAP, São Paulo 01005-010, Brazil
Talles V. Brugni: Finance and Accounting Department, FUCAPE Business School, Vitória 29075-505, Brazil

Mathematics, 2021, vol. 9, issue 10, 1-28

Abstract: Our article explores an underused mathematical analytical methodology in the social sciences. In addition to describing the method and its advantages, we extend a previously reported application of mixed models in a well-known database about corruption in 149 countries. The dataset in the mentioned study included a reasonable amount of zeros (13.19%) in the outcome variable, which is typical of this type of research, as well as quite a bit of social sciences research. In our paper, present detailed guidelines regarding the estimation of models where the data for the outcome variable includes an excess number of zeros, and the dataset has a natural nested structure. We believe our research is not likely to reject the hypothesis favoring the adoption of mixed modeling and the inflation of zeros over the original simpler framework. Instead, our results demonstrate the importance of considering random effects at country levels and the zero-inflated nature of the outcome variable.

Keywords: zero-inflated models; count data models; multilevel models; mixed models; random coefficients models; hierarchical models; random effects; nested models; GLMM (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.mdpi.com/2227-7390/9/10/1100/pdf (application/pdf)
https://www.mdpi.com/2227-7390/9/10/1100/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:9:y:2021:i:10:p:1100-:d:553783

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().