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

Olaomi, JO; Ifederu, A.

doi:10.3923/ajms.2008.14.23

Cited by 7 publications

(2 citation statements)

References 16 publications

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“…This behavior of the t distribution guarantees the robustness of the resulting estimators (Lucas [10], Arslan and Genç [3,4]). Note that as tends to infinity ( ) → 1 and this case gives the estimators given in equations (11)- (13).…”

Section: Parameters Estimation Under T Distributionmentioning

confidence: 97%

“…However, one of the problems in application is that the error term may be correlated with each other. In this case, although, the OLS estimators are unbiased and consistence, they may be no longer efficient even in large sample cases, and hence this may cause large estimated standard errors for the estimators of the regression parameters (see Olaomi and Ifederu [13]). There are many ways to deal with autocorrelated structures in the disturbances; the most common way is to assume autoregressive error terms in regression model.…”

Section: Introductionmentioning

confidence: 99%

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Robust parameter estimation of regression model with AR(p) error terms

Tuaç

Güney

Şenoğlu

et al. 2017

Communications in Statistics - Simulation and Computation

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In this paper, we consider a linear regression model with AR(p) error terms with the assumption that the error terms have a t distribution as a heavy tailed alternative to the normal distribution. We obtain the estimators for the model parameters by using the conditional maximum likelihood (CML) method. We conduct an iteratively reweighting algorithm (IRA) to find the estimates for the parameters of interest. We provide a simulation study and three real data examples to illustrate the performance of the proposed robust estimators based on t distribution.

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Section: Parameters Estimation Under T Distributionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Robust parameter estimation of regression model with AR(p) error terms

Tuaç

Güney

Şenoğlu

et al. 2017

Communications in Statistics - Simulation and Computation

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show abstract

Parameter estimation of regression model with AR(p) error terms based on skew distributions with EM algorithm

2019

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Parameter estimation and the variable selection are two pioneer issues in regression analysis. While traditional variable selection methods require prior estimation of the model parameters, the penalized methods simultaneously carry on parameter estimation and variable select. Therefore, penalized variable selection methods are of great interest and have been extensively studied in literature. However, most of the papers in literature are only limited to the regression models with uncorrelated error terms and normality assumption. In this study, we combine the parameter estimation and the variable selection in regression models with autoregressive error term by using different penalty functions under heavy tailed error distribution assumption. We conduct a simulation study and a real data example to show the performance of the estimators.

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