About: Segmenting mean-nonstationary time series via trending regressions

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An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

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rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
rdfs:seeAlso	http://dx.doi.org/10.1016/j.jeconom.2012.02.003
Description	In this paper, we provide a segmentation procedure for mean-nonstationary time series. The segmentation is obtained by casting the problem into the. framework of detecting structural breaks in trending regression models in which the regressors are generated by suitably smooth functions. As test statistics we propose to use the maximally selected likelihood ratio statistics and a related statistics based on partial sums of weighted residuals. The main theoretical contribution of the paper establishes the extreme value distribution of these statistics and their consistency. To circumvent the slow convergence to the extreme value limit, we propose to employ a version of the circular bootstrap. This procedure is completely data-driven and does not require knowledge of the time series structure. In an empirical part, we show in a simulation study and applications to air carrier traffic and S&P 500 data that the finite sample performance is very satisfactory. In this paper, we provide a segmentation procedure for mean-nonstationary time series. The segmentation is obtained by casting the problem into the. framework of detecting structural breaks in trending regression models in which the regressors are generated by suitably smooth functions. As test statistics we propose to use the maximally selected likelihood ratio statistics and a related statistics based on partial sums of weighted residuals. The main theoretical contribution of the paper establishes the extreme value distribution of these statistics and their consistency. To circumvent the slow convergence to the extreme value limit, we propose to employ a version of the circular bootstrap. This procedure is completely data-driven and does not require knowledge of the time series structure. In an empirical part, we show in a simulation study and applications to air carrier traffic and S&P 500 data that the finite sample performance is very satisfactory. (en)
Title	Segmenting mean-nonstationary time series via trending regressions Segmenting mean-nonstationary time series via trending regressions (en)
skos:prefLabel	Segmenting mean-nonstationary time series via trending regressions Segmenting mean-nonstationary time series via trending regressions (en)
skos:notation	RIV/00216208:11320/12:10124946!RIV13-GA0-11320___
http://linked.open...avai/predkladatel	Matematicko-fyzikální fakulta
http://linked.open...avai/riv/aktivita	I P
http://linked.open...avai/riv/aktivity	I, P(GA201/09/0755)
http://linked.open...iv/cisloPeriodika	2
http://linked.open...vai/riv/dodaniDat	2013
http://linked.open...aciTvurceVysledku	Hušková, Marie
http://linked.open.../riv/druhVysledku	J - Článek v odborném periodiku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	Univerzita Karlova v Praze / Matematicko-fyzikální fakulta
http://linked.open...dnocenehoVysledku	167236
http://linked.open...ai/riv/idVysledku	RIV/00216208:11320/12:10124946
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	Trending regression; Polynomial regression; Linear models; Gumbel distribution; Gaussian processes; Extreme value asymptotics; Circular bootstrap; LINEAR-MODELS; CHANGE-POINT (en)
http://linked.open.../riv/klicoveSlovo	Polynomial regression CHANGE-POINT Circular bootstrap Extreme value asymptotics Gaussian processes Gumbel distribution LINEAR-MODELS Linear models Trending regression
http://linked.open...odStatuVydavatele	NL - Nizozemsko
http://linked.open...ontrolniKodProRIV	[A42D5951FCE9]
http://linked.open...i/riv/nazevZdroje	JOURNAL OF ECONOMETRICS
http://linked.open...in/vavai/riv/obor	BA
http://linked.open...ichTvurcuVysledku	1 (xsd:int)
http://linked.open...cetTvurcuVysledku	3 (xsd:int)
http://linked.open...vavai/riv/projekt	Modelling nonhomogeneous time series
http://linked.open...UplatneniVysledku	2012
http://linked.open...v/svazekPeriodika	168
http://linked.open...iv/tvurceVysledku	Aue, Alexander Horvath, Lajos Hušková, Marie
http://linked.open...ain/vavai/riv/wos	000304634800013
issn	0304-4076
number of pages	15 (xsd:int)
http://bibframe.org/vocab/doi	10.1016/j.jeconom.2012.02.003
http://localhost/t...ganizacniJednotka	11320
is http://linked.open...avai/riv/vysledek of	Segmenting mean-nonstationary time series via trending regressions

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