Robust second-order least-squares estimation for regression models with autoregressive errors
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Rosadi and Peiris (Comput Stat 29:931–943, 2014) applied the second-order least squares estimator (SLS), which was proposed in Wang and Leblanc (Ann Inst of Stat Math 60:883–900, 2008), to regression models with autoregressive errors. In case of autocorrelated errors, it shows that the SLS performs well for estimating the parameters of the model and gives small bias. For less correlated data, the standard error (SE) of the SLS lies between the SE of the ordinary least squares estimator (OLS) and the generalized least squares estimator, however, for more correlated data, the SLS has higher SE than the OLS estimator. In case of a regression model with iid errors, Chen, Tsao and Zhou (Stat Pap 53:371–386, 2012) proposed a method to improve the robustness of the SLS against X-outliers. In this paper, we consider a new robust second-order least squares estimator (RSLS), which extends the study in Chen et al. (2012) to the case of regression with autoregressive errors, and the data may be contaminated with all types of outliers (X-, y-, and innovation outliers). Besides the regression coefficients, here we also propose a robust method to estimate the parameters of the autoregressive errors and the variance of the errors. We evaluate the performance of the RSLS by means of simulation studies. In the simulation study, we consider both a linear and a nonlinear regression model. The results show that the RSLS performs very well. We also provide guidelines to use the RSLS in practice and present a real example.
KeywordsRobust second-order least squares Regression model Autocorrelated errors Ordinary least squares Generalized least squares
Mathematics Subject Classification62J05 62F35
The financial support for D. Rosadi from OeAD - ASEA-UNINET to initiate this project and Hibah KLN - DIKTI 2016 to revise and to finish the report is also gratefully acknowledged. This work has been done while D. Rosadi was visiting Department of Statistics and Probability Theory, Vienna University of Technology, Austria. The authors would like to thank the anonymous referee and the editor of this journal for their constructive comments and useful suggestions to improve the quality and readability of this manuscript.
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