A Simulation Study of Bayesian Estimator for Seemingly Unrelated Regression under Different Distributional Assumptions
Ojo O. Oluwadare, Owonipa R. Oluremi, Enesi O. Lateifat
Asian Journal of Probability and Statistics · pp. 1–8 · Published 25 Jan 2021
10.9734/ajpas/2020/v10i430251Abstract
This paper presents Bayesian analysis of Seemingly Unrelated Regression (SUR) model. An independent prior for parameters was used. The Bayesian method was compared with classical method of estimation to know the most efficient estimator under different distributional assumptions through a simulation study. In order to facilitate comparison among these estimators, Mean Squared Error (MSE) was considered as a criterion. Furthermore, based on the simulation, it was deduced that MSE of the Bayesian estimator is smaller than all the classical methods of estimation for SUR model while Normal distribution was considered as an ideal distribution in generation of disturbances in any simulation study.
Cited by 2
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