Limit Theory for an Explosive Autoregressive Process

Large sample properties are studied for a first-order autoregression (AR(1)) with a root greater than unity. It is shown that, contrary to the AR coefficient, the least-squares (LS) estimator of the intercept and its t-statistic are asymptotically normal without requiring the Gaussian error distribu...

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Main Authors:	WANG, Xiaohu, YU, Jun
Format:	text
Language:	English
Published:	Institutional Knowledge at Singapore Management University 2013
Subjects:	Explosive model Intercept Invariance principle Bubbles Econometrics
Online Access:	https://ink.library.smu.edu.sg/soe_research/1513 https://ink.library.smu.edu.sg/context/soe_research/article/2512/viewcontent/08_2013.pdf
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Institution:	Singapore Management University
Language:	English

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spelling	sg-smu-ink.soe_research-25122019-04-19T09:05:53Z Limit Theory for an Explosive Autoregressive Process WANG, Xiaohu YU, Jun Large sample properties are studied for a first-order autoregression (AR(1)) with a root greater than unity. It is shown that, contrary to the AR coefficient, the least-squares (LS) estimator of the intercept and its t-statistic are asymptotically normal without requiring the Gaussian error distribution, and hence an invariance principle applies. The coefficient based test and the t test have better power for testing the hypothesis of zero intercept in the explosive process than in the stationary process. 2013-11-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/soe_research/1513 https://ink.library.smu.edu.sg/context/soe_research/article/2512/viewcontent/08_2013.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Economics eng Institutional Knowledge at Singapore Management University Explosive model Intercept Invariance principle Bubbles Econometrics
institution	Singapore Management University
building	SMU Libraries
continent	Asia
country	Singapore Singapore
content_provider	SMU Libraries
collection	InK@SMU
language	English
topic	Explosive model Intercept Invariance principle Bubbles Econometrics
spellingShingle	Explosive model Intercept Invariance principle Bubbles Econometrics WANG, Xiaohu YU, Jun Limit Theory for an Explosive Autoregressive Process
description	Large sample properties are studied for a first-order autoregression (AR(1)) with a root greater than unity. It is shown that, contrary to the AR coefficient, the least-squares (LS) estimator of the intercept and its t-statistic are asymptotically normal without requiring the Gaussian error distribution, and hence an invariance principle applies. The coefficient based test and the t test have better power for testing the hypothesis of zero intercept in the explosive process than in the stationary process.
format	text
author	WANG, Xiaohu YU, Jun
author_facet	WANG, Xiaohu YU, Jun
author_sort	WANG, Xiaohu
title	Limit Theory for an Explosive Autoregressive Process
title_short	Limit Theory for an Explosive Autoregressive Process
title_full	Limit Theory for an Explosive Autoregressive Process
title_fullStr	Limit Theory for an Explosive Autoregressive Process
title_full_unstemmed	Limit Theory for an Explosive Autoregressive Process
title_sort	limit theory for an explosive autoregressive process
publisher	Institutional Knowledge at Singapore Management University
publishDate	2013
url	https://ink.library.smu.edu.sg/soe_research/1513 https://ink.library.smu.edu.sg/context/soe_research/article/2512/viewcontent/08_2013.pdf
_version_	1770571705593364480

Limit Theory for an Explosive Autoregressive Process

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