Mean and Autocovariance Function Estimation Near the Boundary of Stationarity

We analyze the applicability of standard normal asymptotic theory for linear process models near the boundary of stationarity. Limit results are given for estimation of the mean, autocovariance and autocorrelation functions within the broad region of stationarity that includes near boundary cases wh...

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Main Authors: GIRAITIS, Liudas, PHILLIPS, Peter C. B.
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2012
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Online Access:https://ink.library.smu.edu.sg/soe_research/1831
https://ink.library.smu.edu.sg/context/soe_research/article/2830/viewcontent/MeanAutocovarianceFunctionEstimation_2012.pdf
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spelling sg-smu-ink.soe_research-28302017-08-05T13:47:44Z Mean and Autocovariance Function Estimation Near the Boundary of Stationarity GIRAITIS, Liudas PHILLIPS, Peter C. B. We analyze the applicability of standard normal asymptotic theory for linear process models near the boundary of stationarity. Limit results are given for estimation of the mean, autocovariance and autocorrelation functions within the broad region of stationarity that includes near boundary cases which vary with the sample size. The rate of consistency and the validity of the normal asymptotic approximation for the corresponding estimators is determined both by the sample size n and a parameter measuring the proximity of the model to the unit root boundary. (C) 2012 Elsevier B.V. All rights reserved. 2012-08-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/soe_research/1831 info:doi/10.1016/j.jeconom.2012.01.020 https://ink.library.smu.edu.sg/context/soe_research/article/2830/viewcontent/MeanAutocovarianceFunctionEstimation_2012.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Economics eng Institutional Knowledge at Singapore Management University Asymptotic normality Integrated periodogram Linear process Local to unity Localizing coefficient Econometrics
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Asymptotic normality
Integrated periodogram
Linear process
Local to unity
Localizing coefficient
Econometrics
spellingShingle Asymptotic normality
Integrated periodogram
Linear process
Local to unity
Localizing coefficient
Econometrics
GIRAITIS, Liudas
PHILLIPS, Peter C. B.
Mean and Autocovariance Function Estimation Near the Boundary of Stationarity
description We analyze the applicability of standard normal asymptotic theory for linear process models near the boundary of stationarity. Limit results are given for estimation of the mean, autocovariance and autocorrelation functions within the broad region of stationarity that includes near boundary cases which vary with the sample size. The rate of consistency and the validity of the normal asymptotic approximation for the corresponding estimators is determined both by the sample size n and a parameter measuring the proximity of the model to the unit root boundary. (C) 2012 Elsevier B.V. All rights reserved.
format text
author GIRAITIS, Liudas
PHILLIPS, Peter C. B.
author_facet GIRAITIS, Liudas
PHILLIPS, Peter C. B.
author_sort GIRAITIS, Liudas
title Mean and Autocovariance Function Estimation Near the Boundary of Stationarity
title_short Mean and Autocovariance Function Estimation Near the Boundary of Stationarity
title_full Mean and Autocovariance Function Estimation Near the Boundary of Stationarity
title_fullStr Mean and Autocovariance Function Estimation Near the Boundary of Stationarity
title_full_unstemmed Mean and Autocovariance Function Estimation Near the Boundary of Stationarity
title_sort mean and autocovariance function estimation near the boundary of stationarity
publisher Institutional Knowledge at Singapore Management University
publishDate 2012
url https://ink.library.smu.edu.sg/soe_research/1831
https://ink.library.smu.edu.sg/context/soe_research/article/2830/viewcontent/MeanAutocovarianceFunctionEstimation_2012.pdf
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