Robustness of Some Estimators of Linear Model with Autocorrelated Error Terms When Stochastic Regressors are Normally Distributed

Ayinde, Kayode; Olaomi, JO

doi:10.22237/jmasm/1209615540

Cited by 4 publications

(4 citation statements)

References 18 publications

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“…Lo g e y t = Lo g e β 0 + t Lo g e β 1 + t 2 Lo g e β 2 + Lo g e μ t (18) Lo g e y t = Lo g e β 0 + t Lo g e β 1 + t 2 Lo g e β 2 + t 3 Lo g e β 3 + Lo g e μ t (19) Lo g e y t = Lo g e β 0 + t Lo g e β 1 + t 2 Lo g e β 2 + t 3 Lo g e β 3 + t 4 Lo g e β 4 + Lo g e μ t (20)…”

Section: Methodsunclassified

“…The independent variable is time, t , and it takes values 1,2,…,64 for CCCOC; 1, 2,…,43 for CCDIC; and 1,2,…,39 for CCDEC. The model regression parameters are; 4 , and the auto-correlated parameter, ρ. The Ordinary Least Square (OLS) Estimator is the most common estimator to estimate the parameters of the Classical Linear Regression Model.…”

Section: Methodsmentioning

confidence: 99%

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Modeling Nigerian Covid-19 cases: A comparative analysis of models and estimators

Ayinde

Lukman

Rauf

et al. 2020

Chaos, Solitons & Fractals

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COVID-19 remains a major pandemic currently threatening all the countries of the world. In Nigeria, there were 1, 932 COVID-19 confirmed cases, 319 discharged cases and 58 deaths as of 30th April 2020. This paper, therefore, subjected the daily cumulative reported COVID-19 cases of these three variables to nine (9) curve estimation statistical models in simple, quadratic, cubic, and quartic forms. It further identified the best of the thirty-six (36) models and used the same for prediction and forecasting purposes. The data collected by the Nigeria Centre for Disease Control for sixty-four (64) days, two (2) months and three (3), were daily monitored and eventually analyzed. We identified the best models to be Quartic Linear Regression Model with an autocorrelated error of order 1 (AR(1)); and found the Ordinary Least Squares, Cochrane Orcutt, Hildreth-Lu, and Prais-Winsten and Least Absolute Deviation (LAD) estimators useful to estimate the models' parameters. Consequently, we recommended the daily cumulative forecast values of the LAD estimator for May and June 2020 with a 99% confidence level. The forecast values are alarming, and so, the Nigerian Government needs to hastily review her activities and interventions towards COVID-19 to provide some tactical and robust structures and measures to avert these challenges.

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Section: Methodsunclassified

Section: Methodsmentioning

confidence: 99%

Modeling Nigerian Covid-19 cases: A comparative analysis of models and estimators

Ayinde

Lukman

Rauf

et al. 2020

Chaos, Solitons & Fractals

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show abstract

“…The use of the OLS estimator in this kind of situation has been reported to produce unbiased but inefficient estimates [144,145]. Consequently, estimators of a linear regression model with AR(1) have been developed and these include the Cochrane Orcutt (CORC), Prais-Winsten (PW) and Hildreth-LU (HILU) estimators [146][147][148]. The efficiency of these estimators has been established to depend on the structure of the explanatory variables [144,149].…”

Section: Methodsmentioning

confidence: 99%

Modeling Covid-19 Cases in West African Countries: A Comparative Analysis of Quartic Curve Estimation Models and Estimators

Ayinde

Bello

Rauf

et al. 2021

Modeling, Control and Drug Development for COVID-19 Outbreak Prevention

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“…Rao and Griliches [32] did one of the earliest Monte-Carlo investigations on the small sample properties of several two-stage regression methods in the context of autocorrelated error terms. Other recent works done on these estimators and the violations of the assumptions of classical linear regression model include that of Ayinde and Iyaniwura [33], Ayinde and Oyejola [34], Ayinde [35], Ayinde and Olaomi [36], Ayinde and Olaomi [37] and Ayinde [38].…”

Section: Z Xt Z Z T X Xt T T T Tmentioning

confidence: 99%

Estimators of Linear Regression Model and Prediction under Some Assumptions Violation

Ayinde¹,

Apata²,

Alaba³

2012

OJS

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The development of many estimators of parameters of linear regression model is traceable to non-validity of the assumptions under which the model is formulated, especially when applied to real life situation. This notwithstanding, regression analysis may aim at prediction. Consequently, this paper examines the performances of the Ordinary Least Square (OLS) estimator, Cochrane-Orcutt (COR) estimator, Maximum Likelihood (ML) estimator and the estimators based on Principal Component (PC) analysis in prediction of linear regression model under the joint violations of the assumption of non-stochastic regressors, independent regressors and error terms. With correlated stochastic normal variables as regressors and autocorrelated error terms, Monte-Carlo experiments were conducted and the study further identifies the best estimator that can be used for prediction purpose by adopting the goodness of fit statistics of the estimators. From the results, it is observed that the performances of COR at each level of correlation (multicollinearity) and that of ML, especially when the sample size is large, over the levels of autocorrelation have a convex-like pattern while that of OLS and PC are concave-like. Also, as the levels of multicollinearity increase, the estimators, except the PC estimators when multicollinearity is negative, rapidly perform better over the levels autocorrelation. The COR and ML estimators are generally best for prediction in the presence of multicollinearity and autocorrelated error terms. However, at low levels of autocorrelation, the OLS estimator is either best or competes consistently with the best estimator, while the PC estimator is either best or competes with the best when multicollinearity level is high（λ<u>></u>0.8 or λ<u><</u>-0.49）

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Robustness of Some Estimators of Linear Model with Autocorrelated Error Terms When Stochastic Regressors are Normally Distributed

Cited by 4 publications

References 18 publications

Modeling Nigerian Covid-19 cases: A comparative analysis of models and estimators

Modeling Nigerian Covid-19 cases: A comparative analysis of models and estimators

Modeling Covid-19 Cases in West African Countries: A Comparative Analysis of Quartic Curve Estimation Models and Estimators

Estimators of Linear Regression Model and Prediction under Some Assumptions Violation

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