Sequential Monitoring for Detection of Breaks in Panel Data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3A10506668" target="_blank" >RIV/00216208:11320/25:10506668 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1007/978-3-031-92383-8_32" target="_blank" >https://doi.org/10.1007/978-3-031-92383-8_32</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-92383-8_32" target="_blank" >10.1007/978-3-031-92383-8_32</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Sequential Monitoring for Detection of Breaks in Panel Data

Popis výsledku v původním jazyce

Procedures for the detection of changes in panel data typically concern offline procedures, i.e., all observations are available at the beginning of the statistical analysis and testing is done retrospectively. The present paper deals with online procedures for detecting a change in the mean of panel data arriving sequentially, with the total number of observations eventually received being random. The procedures are developed using principles of offline detection procedures [5] in combination with those used in online procedures [8]. Some limit properties of the proposed procedures are presented, as well as simulation results exhibiting desirable finite-sample properties. The focus is on high-dimensional panel models, which are high dimensional time series with special properties often encountered in econometrics and financial applications.

Název v anglickém jazyce

Sequential Monitoring for Detection of Breaks in Panel Data

Popis výsledku anglicky

Procedures for the detection of changes in panel data typically concern offline procedures, i.e., all observations are available at the beginning of the statistical analysis and testing is done retrospectively. The present paper deals with online procedures for detecting a change in the mean of panel data arriving sequentially, with the total number of observations eventually received being random. The procedures are developed using principles of offline detection procedures [5] in combination with those used in online procedures [8]. Some limit properties of the proposed procedures are presented, as well as simulation results exhibiting desirable finite-sample properties. The focus is on high-dimensional panel models, which are high dimensional time series with special properties often encountered in econometrics and financial applications.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

<a href="/cs/project/GA23-05737S" target="_blank" >GA23-05737S: Fourierova analýza funkcionálních dat</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název statě ve sborníku

NEW TRENDS IN FUNCTIONAL STATISTICS AND RELATED FIELDS

ISBN

978-3-031-92382-1

ISSN

1431-1968

e-ISSN

—

Počet stran výsledku

8

Strana od-do

259-266

Název nakladatele

SPRINGER INTERNATIONAL PUBLISHING AG

Místo vydání

CHAM

Místo konání akce

Novara

Datum konání akce

25. 6. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001545850800032