A Semiparametric Estimation for the First-Order Nonlinear Autoregressive Time Series Model with Independent and Dependent Errors

被引：0
作者：
Rahman Farnoosh
Mahtab Hajebi
S. Yaser Samadi
机构：
[1] Iran University of Science and Technology,School of Mathematics
[2] Southern Illinois University,Department of Mathematics
来源：
Iranian Journal of Science and Technology, Transactions A: Science | 2019年 / 43卷
关键词：
Semiparametric method; Least squares estimation; Nonlinear autoregressive model; Taylor series expansion; Kernel approach;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
In this paper, the first-order nonlinear autoregressive model is considered and a semiparametric method is proposed to estimate nonlinear regression function for both independent and dependent errors. We use Taylor series expansion which has a parametric framework as a representation of the nonlinear regression function. The least squares method is used for parametric estimation, and then, the obtained nonlinear regression function is adjusted by a nonparametric factor. The nonparametric kernel approach is applied to estimate this nonparametric factor. Some consistency properties and simulated results for the semiparametric estimators in a nonlinear autoregressive function are presented. MSE and AIC criterions are also applied to verify the efficiency of the proposed model. A real data on the sale of fresh foods in New Zealand are analyzed to illustrate the application of the proposed semiparametric method.
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页码：905 / 917
页数：12
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