eprintid: 17781
rev_number: 8
eprint_status: archive
userid: 564
dir: disk0/00/01/77/81
datestamp: 2024-02-16 12:04:10
lastmod: 2024-02-16 12:04:10
status_changed: 2024-02-16 12:04:10
type: article
metadata_visibility: show
creators_name: Karakara, Alhassan A.
creators_name: Osabuohien, E. S. C.
title: Categorical Dependent Variables Estimations With Some
Empirical Applications
ispublished: pub
subjects: H1
subjects: HB
subjects: ZA
divisions: sch_soc
full_text_status: public
keywords: *Probit
*Linear Probability Model (LPM)
*Multinomial Models
*Categorical Outcome Variable
*Binary Models
*Logit
abstract: Microeconomic datasets are usually large, mainly survey data. These data are samples of hundreds of respondents
or group of respondents (e.g., households). The distributions of such data are mostly not normal because some
responses/variables are discrete. Handling such datasets poses some problems of summarizing/reporting the
important features of the data in estimations. This study concentrates on how to handle categorical variables in
estimation/reporting based on theoretical and empirical knacks. This study used Ghana Demographic and Health
Survey data for 2014 for illustration and elaborates on how to interpret results of binary and multinomial outcome
regressions. Comparison is made on the different binary models, and binary logit is found to be weighted over the
other binary models. Multinomial logistic model is best handled when the odds of one outcome versus the other
outcome are independent of other outcome alternatives as verified by the Independent of Irrelevant Alternatives (IIA).
Conclusions and suggestions for handling categorical models are discussed in the study.
date: 2020
date_type: published
publication: Applied Econometric Analysis: Emerging Research and Opportunities
id_number: DOI: 10.4018/978-1-7998-1093-3.ch008
refereed: TRUE
citation:   Karakara, Alhassan A. and Osabuohien, E. S. C.  (2020) Categorical Dependent Variables Estimations With Some Empirical Applications.  Applied Econometric Analysis: Emerging Research and Opportunities.       
document_url: http://eprints.covenantuniversity.edu.ng/17781/1/Categorical%20Dependent.pdf