Efficient estimation in smooth threshold autoregressive(1) models

被引：0
|
作者：
Nur D. [1 ]
Nair G.M. [2 ]
Yatawara N.D. [2 ]
机构：
[1] School of Mathematical and Physical Sciences, University of Newcastle, NSW
[2] Department of Mathematics and Statistics, Curtin University of Technology, Perth, WA
来源：
Journal of Statistical Theory and Practice | 2008年 / 2卷 / 1期
关键词：
Adaptive estimation; Efficient estimation; Locally asymptotically normal; Non-linear time series; Smooth threshold autoregressive; Stationarity;
D O I：
10.1080/15598608.2008.10411862
中图分类号：
学科分类号：
摘要：
Verifiable conditions are given for the existence of efficient estimation in Smooth Threshold Autoregressive models of order 1. The paper establishes local asymptotic normality in the semi-parametric setting which is then used to discuss adaptive and efficient estimates of the models. It is found that the adaptation is satisfied if the error densities are symmetric. Simulation results are presented to compare the conditional least squares estimate with the adaptive and efficient estimates for the models. © 2008 Taylor & Francis Group, LLC. All rights reserved.
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页码：83 / 94
页数：11
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