Classical and Bayesian Approaches For the Zero-Inflated Dynamic Categorical Panel Ordered Probit Model

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dc.contributor.advisorLeo Odiwour Odongoen_US
dc.contributor.advisorAnanda Omutokoh Kubeen_US
dc.contributor.authorWanjiru, John Kung’u
dc.date.accessioned2023-08-09T08:07:18Z
dc.date.available2023-08-09T08:07:18Z
dc.date.issued2023
dc.descriptionA Thesis Submitted in Fulfillment of the Requirement for the Award of the Degree of Doctor of Philosophy (Statistics) in the School of Pure and Applied Sciences of Kenyatta University January, 2023en_US
dc.description.abstractThe Zero inflated ordered categorical data with time series structure are often a characteristic of behavioral research attributed to non-participation decision and zero consumption of substances such as drugs. The existing Semi-parametric zero inflated dynamic panel probit model with selectivity have exhibited biasness and inconsistency in estimators as a result of poor treatment of initial condition and exclusion of selectivity in the unobserved individual effects respectively. The model assumes that the cut points are known to address heaping in the data and therefore cannot be used when the cut points are unknown. The Simulated maximum likelihood was applied to evaluate the double integrals in the Semi-parametric zero inflated dynamic panel probit model. This procedure could be very time-consuming even with fast modern computer and imprecise even with the use of modern simulator like Halton simulators. The aim of this research was to develop the Zero inflated dynamic panel ordered probit models with independent and correlated error terms to address the above challenges. Interpretation of the coefficients in the proposed models were extra difficult than in the normal regression scheme because a shift in one of the variables in the equation is conditioned on other variables and their parameters. Average partial effects that gave the effects on the particular probabilities per unit change in the covariates was proposed to address the above challenge. The integrals were evaluated using Two step Gauss Hermite quadrature that is five times faster than the Simulated maximum likelihood. Since the solutions are not of closed form, maximum likelihood estimation based on Newton Raphson algorithm and Bayesian approach were used to estimate the parameters of the proposed models. Monte Carlo simulations were conducted to investigate the theoretical properties of the estimators of the developed models. Using National Longitudinal Survey of Youth (1997) dataset sponsored by the Bureau of labour Statistics of the U.S. Department of labour with zero inflation, the study investigated the determinants of smoking tobacco among the youths. The study found that the proposed models produced consistent estimators and their estimates were more accurate than the Dynamic panel ordered probit model estimates. The proposed models fitted the data better than dynamic panel ordered probit model in both classical and Bayesian approaches in the simulated data. The study found positive associations between the initial period participation decision and consumption levels observations and unobserved latent participation decision and consumption levels. Therefore, this indicated that it is essential to control for participation decision and consumption levels at the initial period. The models showed a strong and significant positive state dependence in both participation decision and at various consumption levels. The unobserved individual effects accounted for 49.90% of the unexplained variation in decision to participate in smoking and 47.65% of the unexplained variation at all levels of consumption. The main causes of persistence in smoking decision were the state dependence, unobserved heterogeneity and race while the main causes of persistence at consumption level were state dependence, unobserved heterogeneity, gender and age. The study is significant to policy analyst in identifying the socioeconomic and demographic factors associated with drug abuse and providing useful information to facilitate well-targeted public health policies.en_US
dc.description.sponsorshipKenyatta Universityen_US
dc.identifier.urihttp://ir-library.ku.ac.ke/handle/123456789/26666
dc.language.isoenen_US
dc.publisherKenyatta Universityen_US
dc.subjectClassicalen_US
dc.subjectBayesian Approachesen_US
dc.subjectZero-Inflateden_US
dc.subjectDynamicen_US
dc.subjectCategoricalen_US
dc.subjectPanelen_US
dc.subjectOrdereden_US
dc.subjectProbiten_US
dc.subjectModelen_US
dc.titleClassical and Bayesian Approaches For the Zero-Inflated Dynamic Categorical Panel Ordered Probit Modelen_US
dc.typeThesisen_US
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