Estimation of the parameters of linear regression model with autocorrelated error terms which are also correlated with the regressor

Olaomi, JO

doi:10.4314/gjpas.v13i2.16700

Cited by 3 publications

(2 citation statements)

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“…Some researchers have worked on the methods for detecting the presence of autocorrelation and alternative estimators to estimate the parameters in the linear regression model with autocorrelation error. These include Aitken [6], Cochran and Orcutt [2] Nwabueze (2000), Nwabueze [8], Olaomi [9], Olaomi [10], Olaomi and Ifederu [11], Grochova and Strelec (2013). In time-series applications, there are many structures of autocorrelation (Olaomi and Ifederu, 2008).…”

Section: Autocorrelation Problem In a Linear Modelmentioning

confidence: 99%

Robustness Test of the Two Stage K-L Estimator in Models with Multicollinear Regressors and Autocorrelated Error Term

Anono

Osagie

2021

AJPAS

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In a classical multiple linear regression analysis, multicollinearity and autocorrelation are two main basic assumption violation problems. When multicollinearity exists, biased estimation techniques such as Maximum Likelihood, Restricted Maximum Likelihood and most recent the K-L estimator by Kibria and Lukman [1] are preferable to Ordinary Least Square. On the other hand, when autocorrelation exist in the data, robust estimators like Cochran Orcutt and Prais-Winsten [2] estimators are preferred. To handle these two problems jointly, the study combines the K-L with the Prais-Winsten’s two-stage estimator producing the Two-Stage K-L estimator proposed by Zubair & Adenomon [3]. The Mean Square Error (MSE) and Root Mean Square Error (RMSE) criterion was used to compare the performance of the estimators. Application of the estimators to two (2) real life data set with multicollinearity and autocorrelation problems reveals that the Two Stage K-L estimator is generally the most efficient.

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Section: Autocorrelation Problem In a Linear Modelmentioning

confidence: 99%

Robustness Test of the Two Stage K-L Estimator in Models with Multicollinear Regressors and Autocorrelated Error Term

Anono

Osagie

2021

AJPAS

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“…It is known that in an autocorrelated but none endogenized model, the Feasible Generalized Least Square (FGLS) estimator is better than the OLS estimator when it comes to efficiency in their estimates. Two-Stage Least Squares (2SLS) estimator similarly performs better than other estimators with the presence of endogeneity in the model and absence of autocoreelation (Olaomi & Iyaniwura, 2006;Olaomi, 2008). Infractions in predictor disturbance term presumptions contain certain vital components of the OLS model.…”

Section: Introductionmentioning

confidence: 99%

Endogeneity Effect on AR (1) Models in Small Samples

Kanyir¹,

Olaomi²,

Luguterah³

2022

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This study examines the endogeneity effect on autoregressive linear models of AR (1) in small samples, making use of the Ordinary Least Square (OLS) estimator, Two-Stage Least Squares (2SLS) estimator, and Generalized Method of Moment (GMM) estimator, based on the sensitivity analysis of sample size and specification errors in estimator determination in linear regression model through the use of Monte Carlo simulation and application to real-life data. The simulation indicates that 2SLS and GMM estimators show the smallest biases when the sample size is varied from n = 10, 25, 50 to 100. The estimator that performs best when sample size n = 10 across autocorrelation (ρ) and significant correlation (α) at all levels of replication of 10,000 is GMM. In the real-life data, OLS and 2SLS exhibit higher endogeneity characteristics from the dataset used. The empirical analysis base on MSE criteria GMM is the best estimator for dealing with external shock factors to inflations embedded with endogeneity in the linear model. When endogeneity and autocorrelation are bedeviled in a linear AR (1) model, in small samples, using the GMM estimator will provide the best results in small samples than using 2SLS and OLS.

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Understanding Estimators of Linear Regression Model with AR(1) Error Which are Correlated with Exponential Regressor

Olaomi¹,

Ifederu²

2007

Asian J. of Mathematics & Statistics

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Estimation of the parameters of linear regression model with autocorrelated error terms which are also correlated with the regressor

Cited by 3 publications

References 0 publications

Robustness Test of the Two Stage K-L Estimator in Models with Multicollinear Regressors and Autocorrelated Error Term

Robustness Test of the Two Stage K-L Estimator in Models with Multicollinear Regressors and Autocorrelated Error Term

Endogeneity Effect on AR (1) Models in Small Samples

Understanding Estimators of Linear Regression Model with AR(1) Error Which are Correlated with Exponential Regressor

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