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Semiparametric transition models
Econometric Reviews ( IF 1.2 ) Pub Date : 2021-08-03 , DOI: 10.1080/07474938.2021.1957281
Pavel Čížek 1 , Chao Hui Koo 1
Affiliation  

Abstract

A new semiparametric time series model is introduced – the semiparametric transition (SETR) model – that generalizes the threshold and smooth transition models by letting the transition function to be of an unknown form. Estimation is based on a combination of the (local) least squares estimations of the transition function and regression parameters. The asymptotic behavior for the regression coefficient estimator of the SETR model is established, including its oracle property. Monte Carlo simulations demonstrate that the proposed estimator is more robust to the form of the transition function than parametric threshold and smooth transition methods and more precise than varying coefficient estimators.



中文翻译:

半参数过渡模型

摘要

引入了一种新的半参数时间序列模型 - 半参数转换 (SETR) 模型 - 通过让转换函数具有未知形式来推广阈值和平滑转换模型。估计基于转移函数和回归参数的(局部)最小二乘估计的组合。建立了SETR模型回归系数估计量的渐近行为,包括其预言性质。蒙特卡罗模拟表明,所提出的估计器比参数阈值和平滑过渡方法对转换函数的形式更鲁棒,并且比变化系数估计器更精确。

更新日期:2021-08-03
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