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Home  >  Volume 31 (July 2015)

Bayesian Minimum Message Length87 with Parametric Heteroscedasticlinear Model by Oloyede I. (pages pp 35 – 44)
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Abstract

A Metropolis-Hasting algorithm was adapted to perform simulation on marginal posterior distribution of heteroscedastic linear model using Minimum Message Length87 which was conjugated with normal and inverted gamma priors to derive joint posterior distributions. The asymptotic behaviour was compared using absolute bias and mean square error criteria in order to ascertain consistency and efficiency of the estimator. The estimator is both asymptotically consistent and efficient. Results from this study would assist social and behavioural scientists if the methodology is adopted when presence of heteroscedasticity is established. This will enable them to  have good precision of the inferences of the models parameters estimate. The estimator performed better when compare with conventional ordinary least square estimator. The algorithm runs faster in computation.

Keywords: Bayesian Inference, MML87, BMML87,Heteroscedastic, MCMC.

JEL: C52 & JEL C11.

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