Inference on nonstationary time series with moving mean

Jiti Gao, Peter M Robinson

Research output: Contribution to journalArticleResearchpeer-review

Abstract

A semiparametric model is proposed in which a parametric filtering of a nonstationary time series, incorporating fractionally differencing with short memory correction, removes correlation but leaves a nonparametric deterministic trend. Estimates of the memory parameter and other dependence parameters are proposed, and shown to be consistent and asymptotically normally distributed with parametric rate. Tests with standard asymptotics for I(1) and other hypotheses are thereby justified. Estimation of the trend function is also considered. We include a Monte Carlo study of finite-sample performance.
Original languageEnglish
Pages (from-to)431 - 457
Number of pages27
JournalEconometric Theory
Volume32
Issue number2
DOIs
Publication statusPublished - 2016

Cite this

Gao, Jiti ; Robinson, Peter M. / Inference on nonstationary time series with moving mean. In: Econometric Theory. 2016 ; Vol. 32, No. 2. pp. 431 - 457.
@article{7405b1d1846946da9ee1ed3153ae1835,
title = "Inference on nonstationary time series with moving mean",
abstract = "A semiparametric model is proposed in which a parametric filtering of a nonstationary time series, incorporating fractionally differencing with short memory correction, removes correlation but leaves a nonparametric deterministic trend. Estimates of the memory parameter and other dependence parameters are proposed, and shown to be consistent and asymptotically normally distributed with parametric rate. Tests with standard asymptotics for I(1) and other hypotheses are thereby justified. Estimation of the trend function is also considered. We include a Monte Carlo study of finite-sample performance.",
author = "Jiti Gao and Robinson, {Peter M}",
year = "2016",
doi = "10.1017/S0266466614000875",
language = "English",
volume = "32",
pages = "431 -- 457",
journal = "Econometric Theory",
issn = "0266-4666",
publisher = "Cambridge University Press",
number = "2",

}

Inference on nonstationary time series with moving mean. / Gao, Jiti; Robinson, Peter M.

In: Econometric Theory, Vol. 32, No. 2, 2016, p. 431 - 457.

Research output: Contribution to journalArticleResearchpeer-review

TY - JOUR

T1 - Inference on nonstationary time series with moving mean

AU - Gao, Jiti

AU - Robinson, Peter M

PY - 2016

Y1 - 2016

N2 - A semiparametric model is proposed in which a parametric filtering of a nonstationary time series, incorporating fractionally differencing with short memory correction, removes correlation but leaves a nonparametric deterministic trend. Estimates of the memory parameter and other dependence parameters are proposed, and shown to be consistent and asymptotically normally distributed with parametric rate. Tests with standard asymptotics for I(1) and other hypotheses are thereby justified. Estimation of the trend function is also considered. We include a Monte Carlo study of finite-sample performance.

AB - A semiparametric model is proposed in which a parametric filtering of a nonstationary time series, incorporating fractionally differencing with short memory correction, removes correlation but leaves a nonparametric deterministic trend. Estimates of the memory parameter and other dependence parameters are proposed, and shown to be consistent and asymptotically normally distributed with parametric rate. Tests with standard asymptotics for I(1) and other hypotheses are thereby justified. Estimation of the trend function is also considered. We include a Monte Carlo study of finite-sample performance.

U2 - 10.1017/S0266466614000875

DO - 10.1017/S0266466614000875

M3 - Article

VL - 32

SP - 431

EP - 457

JO - Econometric Theory

JF - Econometric Theory

SN - 0266-4666

IS - 2

ER -