The Efficiency Of OLS In The Presence Of Auto-Correlated Disturbances In Regression Models

Safi, Samir K.; White, Alexander K

doi:10.22237/jmasm/1146456540

Cited by 9 publications

(6 citation statements)

References 15 publications

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“…Therefore, the transformation model (12) satisfies the assumption of error ( ) . For model (12) the LS estimator, which called the Two-stage Prais-Winsten (TP) [12]…”

Section: Sincementioning

confidence: 99%

A Monte Carlo Study for Dealing with Multicollinearity and Autocorrelation Problems in Linear Regression Using Two Stage Ridge Regression Method

Eledum¹,

Awadallah²

2021

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In the multiple linear regression model, the problem of multicollinearity may come together with autocorrelation; therefore, several methods of estimation are developed to deal with this case; Two-Stage Ridge Regression (TR) is one of them. This article's main objective is to run a Monte Carlo simulation to investigate the impact of both problems, Multicollinearity and Autocorrelation, in multiple linear regression model on the performance of the TR. The simulation is carried out under different levels of multicollinearity, and sets of autocorrelation coefficient, taking into account different sample sizes. Some new properties for the TR method, including expectation, variance and mean square error, are droved. In contrast, the study also has developed some techniques to estimate the biasing parameter for the TR by modifying some popular techniques used in ridge regression (RR). Moreover, Mean Square Error is used as a base for evaluation and comparison. The empirical findings from the simulations revealed that the TR estimator performs better than the RR, and the values of the biasing parameter under the TR are always less than that under the RR. This paper contributes to the existing literature on developing new estimation methods used to overcome the presence of mixed problems in a linear regression model and studying their properties.

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“…Therefore, the transformation model (12) satisfies the assumption of error ( ) . For model (12) the LS estimator, which called the Two-stage Prais-Winsten (TP) [12]…”

Section: Sincementioning

confidence: 99%

A Monte Carlo Study for Dealing with Multicollinearity and Autocorrelation Problems in Linear Regression Using Two Stage Ridge Regression Method

Eledum¹,

Awadallah²

2021

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“…For this reason, we focus on the literatures that have dealt with this kind of data. Concerning the use of the Maximum Likelihood (ML), ISSN 2161-7104 2021 Weighted Generalized Least squares ) WGLS), Generalized Least Squares (GLS), Feasible Generalized Least Squares (FGLS) estimation methods, (Mourad M. , 2017) did both theoretical and practical studies, (Chinonso, Oluchukwu, Charity, Nnaemeka, & Chukwunenye, 2020) provided an extension of the ARIMA models when the generated residuals are considered a Fourier series, modeling Malaria Incidence Rates, (Safi S. , 2004) studied the efficiency of the OLS, GLS and estimated GLS (EGLS) estimators when the disturbances reveal a first and second order autoregressive models, (Safi & Abu Saif, 2014) compared the prediction using the GLS method for parameter estimation in the regression models with autocorrelated disturbances considering real data and adopting an ARIMA model for the time series, (Lee & Lund, 2004) proposed the properties of OLS and GLS estimators in a simple linear regression with stationary autocorrelated errors, (Mandy & Fridli, 2001) show under very parsimonious assumptions that FGLS and GLS are asymptotically equivalent when errors follow an invertible MA(1) process.…”

Section: Review Of Literaturementioning

confidence: 99%

Impact of Socio-Economic Variables on Life Expectancy: An Empirical Study for 138 Countries

Mourad¹

2021

JPAG

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This study has examined the impact of mortality rate of children under five years of age (MORRATE), physicians (PMP), health expenditure per person (HEPP), access to electricity (AELEC) and GDP per capita on life expectancy at birth (LEB) for one hundred and thirty-eight countries taken as cross-sectional data. The MORRATE ranged from 2.4 to 160.2 (per 1,000 people), thus reflecting an inequality in LEB which fluctuates between 44.8 and 82.8. The PMP varies from 0.01 to 7.74, the HEPP between 16.92 and 8264 USD, the AELEC between 4.1% to 100% and finally the GDP per capita oscillates between 326.6 and 102,863 USD. The multiple linear regression model is estimated using the OLS method and several tests for heteroscedasticity are performed. The null hypothesis of homoscedasticity is rejected and therefore the Weighted Generalized Least Squares) WGLS) method is used to produce unbiased, efficient and consistent estimators. The results showed a negative impact of MORRATE on LEB. A single increase in the number of deceased children leads to a decrease of about 2.12 months in LEB. The HEPP has a positive impact on LEB, so if HEPP rises to 100 USD then the LEB rises by 33 days approximately. When introducing four binary variables characterizing the five continents, and taking Oceania as a reference, the life expectancy in an African country will be about 2.4 years less than the LEB reference. For the other continents, it seems that the values of LEB are very close.

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“…In this passage, OLS regression will be examined with regards to a bivariate model, that is, a model wherein there is just a single autonomous variable (X) anticipating a needy variable (Y). Not with standing, the rationale of OLS relapse is effortlessly stretched out to the multivariate model where there are at least two free factors (Safi & White, 2006). The simple linear regression model is:…”

Section: B Ordinary Least Squaresmentioning

confidence: 99%

Variation Comparison of OLS and GLS Estimators using Monte Carlo Simulation of Linear Regression Model with Autoregressive Scheme

AliKhan¹,

Khurshid²,

Akhtar³

et al. 2021

QAJ

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In this research we discusses to Ordinary Least Squares and Generalized Least Squares techniques and estimate with First Order Autoregressive scheme from different correlation levels by using simple linear regression model. A comparison has been made between these two methods on the basis of variances results. For the purpose of comparison, we use simulation of Monte Carlo study and the experiment is repeated 5000 times. We use sample sizes 50, 100, 200, 300 and 500, and observe the influence of different sample sizes on the estimators. By comparing variances of OLS and GLS at different values of sample sizes and correlation levels with , we found that variance of ( ) at sample size 500, OLS and GLS gives similar results but at sample size 50 variance of GLS ( ) has minimum values as compared to OLS. So it is clear that variance of GLS ( ) is best. Similarly variance of ( ) from OLS and GLS at sample size 500 and correlation -0.05 with , GLS give minimum value as compared to all other sample sizes and correlations. By comparing overall results of Ordinary Least Squares (OLS) and Generalized Least Squares (GLS), we conclude that in large samples both are gives similar results but small samples GLS is best fitted as compared to OLS.

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The Efficiency Of OLS In The Presence Of Auto-Correlated Disturbances In Regression Models

Cited by 9 publications

References 15 publications

A Monte Carlo Study for Dealing with Multicollinearity and Autocorrelation Problems in Linear Regression Using Two Stage Ridge Regression Method

A Monte Carlo Study for Dealing with Multicollinearity and Autocorrelation Problems in Linear Regression Using Two Stage Ridge Regression Method

Impact of Socio-Economic Variables on Life Expectancy: An Empirical Study for 138 Countries

Variation Comparison of OLS and GLS Estimators using Monte Carlo Simulation of Linear Regression Model with Autoregressive Scheme

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