Two-parameter ridge estimation in seemingly unrelated regression models

Esfanjani, Robab Mehdizadeh; Najarzadeh, Dariush; Khamnei, Hossein Jabbari; Hormozinejad, Farshin; Talebi, Mahnaz

doi:10.1080/03610918.2020.1749662

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…From Tables 2 to 13 and Figures 1 to 4, we can see that the proposed KL S UR estimator uniformly dominate the RFGLS and R S UR estimator except when the sample size is small (n = 20). Also, an increase in the ρ x i x i increases the estimated TMSE values of the estimators.…”

Section: Discussion Of Resultsmentioning

confidence: 93%

“…It is a statistical fallacy to assume that the relationship between or among explanatory variables plays an insignificant effect on the error structure of the model. The severity of the correlation levels among the predictors can affect the efficiency and sensitivity of the estimators [12], [13]. Hence, the variance of the estimator is inflated, unreliable inference and the confidence interval due to multicollinearity is wider which may increase the probability of a type-II error in hypothesis testing of unknown parameters [14].…”

Section: Introductionmentioning

confidence: 99%

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The Efficiency of the K-L Estimator for the Seemingly Unrelated Regression Model: Simulation and Application

Alaba

Kibria

2023

J. Nig. Soc. Phys. Sci.

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This paper considers the Ridge Feasible Generalized Least Squares Estimator (RFGLSE), Ridge Seemingly Unrelated Regression RSUR and proposes the Kibria-Lukman KLSUR estimator for the parameters of the Seemingly Unrelated Regression (SUR) model when the regressors of the models are collinear. A simulation study was conducted to compare the performance of the three different types of estimators for the SUR model. Different correlation levels (0.0, 0.1, 0.2, ..., 0.9) among the independent variables, sample sizes replicated 10000 times and contemporaneous error correlation (0.0, 0.1, 0.2, ..., 0.9) among the equations were assumed for the simulation study. The efficiency of the three (RFGLSE, RSUR, and KLSUR estimators for SUR, when the predictors are correlated, was investigated using the Trace Mean Square Error (TMSE). The results showed that the KLSUR estimator outperformed the other estimators except for a few cases when the sample size is small.

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Section: Discussion Of Resultsmentioning

confidence: 93%