Estimation in semiparametric time series regression

Jia Chen; Jiti Gao; Degui Li

doi:10.4310/SII.2011.v4.n2.a18

Estimation in semiparametric time series regression

Jia Chen, Jiti Gao^*, Degui Li

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

Abstract

In this paper, we consider a semiparametric time series regression model and establish a set of identification conditions such that the model under discussion is both identifiable and estimable. We estimate the parameters in the model by using the method of moment and the nonlinear function by using the local linear method, and establish the asymptotic distributions for the proposed estimators. We then discuss how to estimate a sequence of local departure functions nonparametrically when the null hypothesis is rejected and establish some related asymptotic theory. Both the simulation study and the empirical application are also provided to illustrate the finite sample behavior of the proposed models and methods.

Original language	English
Pages (from-to)	243-251
Number of pages	9
Journal	Statistics and its interface
Volume	4
Issue number	2
DOIs	https://doi.org/10.4310/SII.2011.v4.n2.a18
Publication status	Published - 2011

Keywords

Departure function
MODELS
SINGLE-INDEX
Semiparametric modelling
TEMPERATURE SERIES
Asymptotic distribution
TREND
Local linear method

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10.4310/SII.2011.v4.n2.a18

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abstract = "In this paper, we consider a semiparametric time series regression model and establish a set of identification conditions such that the model under discussion is both identifiable and estimable. We estimate the parameters in the model by using the method of moment and the nonlinear function by using the local linear method, and establish the asymptotic distributions for the proposed estimators. We then discuss how to estimate a sequence of local departure functions nonparametrically when the null hypothesis is rejected and establish some related asymptotic theory. Both the simulation study and the empirical application are also provided to illustrate the finite sample behavior of the proposed models and methods.",

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AU - Gao, Jiti

AU - Li, Degui

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AB - In this paper, we consider a semiparametric time series regression model and establish a set of identification conditions such that the model under discussion is both identifiable and estimable. We estimate the parameters in the model by using the method of moment and the nonlinear function by using the local linear method, and establish the asymptotic distributions for the proposed estimators. We then discuss how to estimate a sequence of local departure functions nonparametrically when the null hypothesis is rejected and establish some related asymptotic theory. Both the simulation study and the empirical application are also provided to illustrate the finite sample behavior of the proposed models and methods.

KW - Departure function

KW - MODELS

KW - SINGLE-INDEX

KW - Semiparametric modelling

KW - TEMPERATURE SERIES

KW - Asymptotic distribution

KW - TREND

KW - Local linear method

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VL - 4

SP - 243

EP - 251

JO - Statistics and its interface

JF - Statistics and its interface

IS - 2

ER -

Estimation in semiparametric time series regression

Abstract

Keywords

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