The Prediction Performance of the Alternative Biased Estimators for the Distributed Lag Models

The finite distributed lag models include highly correlated variables, lagged and unlagged values of the same variables. Some problems are faced for this model when applying the ordinary least squares method or econometric models such as Almon models. Gültay and Kaçıranlar (J Math Stat 44:1215–1233, 2015) compared the performance of the alternative biased estimators to the Almon estimator in terms of the mean square error. The primary aim of this study is to evaluate the predictive performance of the alternative biased estimators to the Almon estimator according to the prediction mean square error criterion under the target function. We use the Almon (Econometrica 178–196, 1965) data to illustrate our theoretical results. © 2019, Shiraz University

Feasible Generalized Stein-Rule Restricted Ridge Regression Estimators

WOS: 000404335200005Several versions of the Stein-rule estimators of the coefficient vector in a linear regression model are proposed in the literature. In the present paper, we propose new feasible generalized Stein-rule restricted ridge regression estimators to examine multicollinearity and autocorrelation problems simultaneously for the general linear regression model, when certain additional exact restrictions are placed on these coefficients. Moreover, a Monte Carlo simulation experiment is performed to investigate the performance of the proposed estimator over the others

Detecting influential observations in Liu and modified Liu estimators

In regression, detecting anomalous observations is a significant step for model-building process. Various influence measures based on different motivational arguments are designed to measure the influence of observations through different aspects of various regression models. The presence of influential observations in the data is complicated by the existence of multicollinearity. The purpose of this paper is to assess the influence of observations in the Liu [9] and modified Liu [15] estimators by using the method of approximate case deletion formulas suggested by Walker and Birch [14]. A numerical example using a real data set used by Longley [10] and a Monte Carlo simulation are given to illustrate the theoretical results. © 2013 Copyright Taylor and Francis Group, LLC

A new stochastic mixed ridge estimator in linear regression model

Ordinary ridge estimator, Ordinary mixed estimator, Stochastic mixed ridge estimator, Mean squared error matrix, 62J05, 62F30,

An alternative stochastic restricted Liu estimator in linear regression

Liu estimator, Mixed estimator, Stochastic restricted Liu estimator, Mean squared error matrix,