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Categorical Dependent Variables Estimations With Some Empirical Applications

Karakara, Alhassan A. and Osabuohien, E. S. C. (2020) Categorical Dependent Variables Estimations With Some Empirical Applications. Applied Econometric Analysis: Emerging Research and Opportunities.

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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.

Item Type: Article
Uncontrolled Keywords: *Probit *Linear Probability Model (LPM) *Multinomial Models *Categorical Outcome Variable *Binary Models *Logit
Subjects: H Social Sciences > H Social Sciences (General)
H Social Sciences > HB Economic Theory
Z Bibliography. Library Science. Information Resources > ZA Information resources
Divisions: Faculty of Law, Arts and Social Sciences > School of Social Sciences
Depositing User: nwokealisi
Date Deposited: 16 Feb 2024 12:04
Last Modified: 16 Feb 2024 12:04

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