Search

The Measurement Error Model (MEM) is employed to fit the relationship between two or more variables when all variables are subject to measurement errors. In the specific case of only two variables, this model is referred to as the Error in Variables model. This paper proposes two new estimation methods for a multiple structural measurement error model, applicable when all variables are subject to errors. The proposed methods, the Repetitive Weighted Grouping and the Iterative Weighted Grouping, are extensions of the Wald estimation method. To evaluate the performance of these new estimators compared to classical estimators-namely, the Maximum Likelihood Estimator (MLE) and the Method of Moments (MOM), a Monte Carlo experiment was conducted. The simulation results showed that the proposed estimators outperform the classical estimators in terms of root mean square error and bias. Additionally, real data analysis was performed to assess the relationships between national GDP, unemployment rate, and human development index using the proposed estimation methods. The results reveal that, based on mean square error (MSE), the proposed methods with r =3 and r =4 yield more accurate estimators than other methods in weight case 1, while the proposed method with r =4 proves more accurate in weight case 2. Furthermore, the proposed procedures demonstrate greater efficient than MLE and MOM in fitting the model. [ABSTRACT FROM AUTHOR]

AbstractThis paper proposes two new estimation methods to fit a multiple structural measurement error model when all variables are subject to errors. The new estimation methods were extensions of the Wald estimation method, one is the weighted grouping method, and the other is the iterative method. A Monte Carlo experiment is performed to investigate the performance of the new estimators compared with the classical estimation methods; the Maximum Likelihood Estimator and Method of Moment, in terms of root mean square error and its bias. The simulation outcomes demonstrated that the suggested estimators are more effective than conventional estimators. In addition, real data analysis is discussed to examine the relationship between the national gross domestic product, unemployment rate, and human development index after applying the two proposed estimation methods. [ABSTRACT FROM AUTHOR]

This article proposes a new estimation method to fit the structural regression model when the variables are subject to errors. The new estimation method is the extension of theWald estimation method and involves iterative process. Several Monte Carlo simulation experiments were used to study the performance of the proposed estimators. The results were compared with the classical Wald estimation method in terms of its root mean square error (RMSE). In addition, an application for examining the relationships between Jordan's national gross domestic product (GDP) and its human development index (HDI) was presented. Numerical results showed that the GDP and HDI have a strong positive and significant correlation. Moreover, the proposed procedures with different subgroup sizes (r =3 and r =4) gave more accurate estimators than the classical estimation methods in fitting the relationships between GDP and HDI. [ABSTRACT FROM AUTHOR]