Estimation in semi-parametric regression with non-stationary regressors

被引：26

作者：

Chen, Jia ^{[1
]}

Gao, Jiti ^{[1
]}

Li, Degui ^{[1
]}

机构：

[1] Univ Adelaide, Sch Econ, Adelaide, SA 5005, Australia

来源：

BERNOULLI | 2012年 / 18卷 / 02期

基金：

澳大利亚研究理事会;

关键词：

asymptotic theory; nonparametric estimation; null recurrent time series; semi-parametric regression; NONPARAMETRIC-ESTIMATION; TIME-SERIES; MODELS;

D O I：

10.3150/10-BEJ344

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

In this paper, we consider a partially linear model of the form Y-t = X-t(tau)theta(0) + g(V-t) + epsilon(t), t = 1,...,n, where {V-t} is a beta null recurrent Markov chain, {X-t} is a sequence of either strictly stationary or non-stationary regressors and {epsilon(t)} is a stationary sequence. We propose to estimate both theta(0) and g(.) by a semi-parametric least-squares (SLS) estimation method. Under certain conditions, we then show that the proposed SLS estimator of theta(0) is still asymptotically normal with the same rate as for the case of stationary time series. In addition, we also establish an asymptotic distribution for the nonparametric estimator of the function g(.). Some numerical examples are provided to show that our theory and estimation method work well in practice.

引用

页码：678 / 702

页数：25

共 34 条

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