Structural change estimation in time series regressions with endogenous variables

We propose to apply the group fused Lasso to estimate time series models with endogenous regressors and an unknown number of breaks. It can correctly determine the number of breaks and estimate the break dates asymptotically. Simulations and applications are given.

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Main Authors:	QIAN, Junhui, SU, Liangjun
Format:	text
Language:	English
Published:	Institutional Knowledge at Singapore Management University 2014
Subjects:	Group fused Lasso Multiple breaks Penalized least squares Penalized GMM Structural change Econometrics Economics
Online Access:	https://ink.library.smu.edu.sg/soe_research/1624 https://ink.library.smu.edu.sg/context/soe_research/article/2623/viewcontent/StructuralChangeEstTSREndoVar_2014.pdf
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Institution:	Singapore Management University
Language:	English

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spelling	sg-smu-ink.soe_research-26232020-04-02T05:18:17Z Structural change estimation in time series regressions with endogenous variables QIAN, Junhui SU, Liangjun We propose to apply the group fused Lasso to estimate time series models with endogenous regressors and an unknown number of breaks. It can correctly determine the number of breaks and estimate the break dates asymptotically. Simulations and applications are given. 2014-12-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/soe_research/1624 info:doi/10.1016/j.econlet.2014.10.021 https://ink.library.smu.edu.sg/context/soe_research/article/2623/viewcontent/StructuralChangeEstTSREndoVar_2014.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Economics eng Institutional Knowledge at Singapore Management University Group fused Lasso Multiple breaks Penalized least squares Penalized GMM Structural change Econometrics Economics
institution	Singapore Management University
building	SMU Libraries
continent	Asia
country	Singapore Singapore
content_provider	SMU Libraries
collection	InK@SMU
language	English
topic	Group fused Lasso Multiple breaks Penalized least squares Penalized GMM Structural change Econometrics Economics
spellingShingle	Group fused Lasso Multiple breaks Penalized least squares Penalized GMM Structural change Econometrics Economics QIAN, Junhui SU, Liangjun Structural change estimation in time series regressions with endogenous variables
description	We propose to apply the group fused Lasso to estimate time series models with endogenous regressors and an unknown number of breaks. It can correctly determine the number of breaks and estimate the break dates asymptotically. Simulations and applications are given.
format	text
author	QIAN, Junhui SU, Liangjun
author_facet	QIAN, Junhui SU, Liangjun
author_sort	QIAN, Junhui
title	Structural change estimation in time series regressions with endogenous variables
title_short	Structural change estimation in time series regressions with endogenous variables
title_full	Structural change estimation in time series regressions with endogenous variables
title_fullStr	Structural change estimation in time series regressions with endogenous variables
title_full_unstemmed	Structural change estimation in time series regressions with endogenous variables
title_sort	structural change estimation in time series regressions with endogenous variables
publisher	Institutional Knowledge at Singapore Management University
publishDate	2014
url	https://ink.library.smu.edu.sg/soe_research/1624 https://ink.library.smu.edu.sg/context/soe_research/article/2623/viewcontent/StructuralChangeEstTSREndoVar_2014.pdf
_version_	1770572446588469248

Structural change estimation in time series regressions with endogenous variables

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