E ciency of bayesian heteroscedastic linear model

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dc.contributor.authorOloyede, Isiaka
dc.contributor.authorIpinyomi, R.A
dc.contributor.authorIyaniwura, J.O.
dc.date.accessioned2021-05-05T08:35:11Z
dc.date.available2021-05-05T08:35:11Z
dc.date.issued2014
dc.descriptionRecently, numerous literatures emerged in the eld of Bayesian Statistics; this is due to the e ort of people like George Casella and Christian Robert, Jim Albert, Reuven Rubinstein, Dirk Kroese and other numerous researchers who brought into limelight the simulation techniques of Markov Chain Monte Carlo technique into the eld of Bayesian Statistics in the early 90s.en_US
dc.description.abstractIn order to investigate the asymptotic e ciency of estimators under two di erent simulation techniques, normal-normal double sided Heteroscedas- tic error structure was adopted. We explored Direct Monte Carlo method of Zellner et al. (2010) and Metropolis Hasting Algorithm experiments, an approach of Markov Chain Monte Carlo. We truncated the model with one error component of two sided error struc- ture. A Metropolis-Hasting Algorithm and Direct Monte Carlo adopted to perform simulation on marginal posterior distribution of heteroscedastic lin- ear econometric model. Since Ordinary Least squares is invalid and inef- cient in the presence of heteroscedastic, heteroscedastic linear model was conjugated with informative priors to form posterior distribution. Maximum Likelihood Estimation was compared with Bayesian Maximum Likelihood Estimation, Mean Squares Error criterion was use to identify which esti- mator and/or simulation method outperform other. We chose the following sample sizes: 25; 50; 100; and 200. Thus 10,000 simulations with varying degree of heteroscedastic error structures were adopted. This is subject to the level of convergence. In the overall, minimum mean squares error criterion revealed improving performance asymptotically regardless of the degree of heteroscedasticity. The results showed that Direct Monte Carlo Method outperformed Markov Chain Monte Carlo Method and Maximum Likelihood Estimator with mini- mum mean square error at any degree of heteroscedasticity.en_US
dc.description.sponsorshipselfen_US
dc.identifier.issn2070-5948
dc.identifier.urihttps://uilspace.unilorin.edu.ng/handle/20.500.12484/4881
dc.language.isoenen_US
dc.publisherElectronic Journal of Applied Statistical Analysis EJASA, Electron. J. App. Stat. Anal.en_US
dc.relation.ispartofseries;2
dc.subjectMarkov Chain Monte Carlo Method, Heteroscedasticity, Bayesian Maximum Likelihood Estimator, Metropolis-Hasting Algorithm, Direct Monte Carlo Method.en_US
dc.titleE ciency of bayesian heteroscedastic linear modelen_US
dc.typeArticleen_US

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