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http://dx.doi.org/10.5351/KJAS.2016.29.1.157

A comparison study on regression with stationary nonparametric autoregressive errors  

Yu, Kyusang (Department of Applied Statistics, Konkuk University)
Publication Information
The Korean Journal of Applied Statistics / v.29, no.1, 2016 , pp. 157-169 More about this Journal
Abstract
We compare four methods to estimate a regression coefficient under linear regression models with serially correlated errors. We assume that regression errors are generated with nonlinear autoregressive models. The four methods are: ordinary least square estimator, general least square estimator, parametric regression error correction method, and nonparametric regression error correction method. We also discuss some properties of nonlinear autoregressive models by presenting numerical studies with typical examples. Our numerical study suggests that no method dominates; however, the nonparametric regression error correction method works quite well.
Keywords
nonparametric autoregressive model; regression; efficiency;
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