On Seemingly Unrelated Regression and Single Equation Estimators under Heteroscedastic Error and Non-Gaussian Responses.

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dc.contributor.authorAfolayan, Razaq Bayo
dc.contributor.authorBanjoko, Alabi Waheed
dc.contributor.authorGarba, Mohammed Kabir
dc.contributor.authorYahya, Waheed Babatunde
dc.date.accessioned2021-10-12T11:33:47Z
dc.date.available2021-10-12T11:33:47Z
dc.date.issued2020
dc.description.abstract- This study investigated the efficiency of Seemingly Unrelated Regression (SUR) estimator of Feasible Generalized Least Square(FGLS) compared to robust MM-BISQ, M-Huber, and Ordinary Least Squares (OLS) estimators when the variances of the error terms are non-constant and the distribution of the response variables is not Gaussian. The finite properties and relative performance of these other estimators to OLS were examined under four forms of heteroscedasticity of the error terms, levels of Contemporaneous Correlation (Cc) with gamma responses. The efficiency of four estimation techniques for the SUR model was examined using the Root Mean Square Error (RMSE) criterion to determine the best estimator(s) under different conditions at various sample sizes. The simulation results revealed that the SUR estimator (FGLS) showed superior performance in the small sample situations when the contemporaneous correlation (ρ) is almost perfect (ρ=0.95) with the gamma response model while MM-BISQ was the best under low contemporaneous correlation. The relative efficiencies of MM-BISQ, M-Huber and FGLS estimators over the OLS are respectively 89%, 71%, and 14% in a small sample (𝑛 ≤ 30) and 49%, 32% and 1% in large sample sizes (𝑛 > 30) under gamma response model. The study concluded that MM-BISQ and M-Huber estimators are the most efficient estimators for modeling systems of simultaneous equations with non-Gaussian responses under either homoscedastic or multiplicative heteroscedastic error terms irrespective of the sample size.en_US
dc.identifier.issn2579-0625 (Online)
dc.identifier.urihttps://uilspace.unilorin.edu.ng/handle/20.500.12484/6586
dc.publisherFUOYE Journal of Engineering and Technologyen_US
dc.relation.ispartofseriesVol. 5(2);2579-0617
dc.subjectContemporaneous correlationen_US
dc.subjectFeasible Generalized Least Squareen_US
dc.subjectHeteroscedasticityen_US
dc.subjectHomoscedasticityen_US
dc.subjectSeemingly unrelated Regressionen_US
dc.titleOn Seemingly Unrelated Regression and Single Equation Estimators under Heteroscedastic Error and Non-Gaussian Responses.en_US
dc.typeArticleen_US

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