Segmenting mean-nonstationary time series via trending regressions

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F12%3A10124946" target="_blank" >RIV/00216208:11320/12:10124946 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.jeconom.2012.02.003" target="_blank" >http://dx.doi.org/10.1016/j.jeconom.2012.02.003</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.jeconom.2012.02.003" target="_blank" >10.1016/j.jeconom.2012.02.003</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Segmenting mean-nonstationary time series via trending regressions

Popis výsledku v původním jazyce

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 theextreme 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 requireknowledge 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.

Název v anglickém jazyce

Segmenting mean-nonstationary time series via trending regressions

Popis výsledku anglicky

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 theextreme 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 requireknowledge 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.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

BA - Obecná matematika

OECD FORD obor

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Projekt

<a href="/cs/project/GA201%2F09%2F0755" target="_blank" >GA201/09/0755: Modelování nehomogenních časových řad</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2012

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

JOURNAL OF ECONOMETRICS

ISSN

0304-4076

e-ISSN

—

Svazek periodika

168

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

15

Strana od-do

367-381

Kód UT WoS článku

000304634800013

EID výsledku v databázi Scopus

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