Omnibus test for covariate effects in conditional copula models

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F21%3A10435376" target="_blank" >RIV/00216208:11320/21:10435376 - isvavai.cz</a>

Výsledek na webu

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=gar6w2_~8v" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=gar6w2_~8v</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.jmva.2021.104804" target="_blank" >10.1016/j.jmva.2021.104804</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Omnibus test for covariate effects in conditional copula models

Popis výsledku v původním jazyce

Conditional copulas describe the conditional dependence and the influence that covariates have on the dependence structure between two (or more) variables. Of interest is to test the null hypothesis that the covariates have a specific effect. This paper proposes an omnibus test for testing the null hypothesis of a specified effect of the covariates. The test statistic is designed for having power against many alternatives, and can be used to test for a variety of covariate effects (no effects, linear effects, partial effects, etc.). A special case is the testing problem that the covariates do not affect the dependence structure. In this semiparametric framework the marginal distribution functions are estimated using nonparametric kernel techniques and the parametric dependence model is estimated using maximum likelihood estimation. We establish the asymptotic distribution of the test statistic under the null hypothesis, and evaluate the finite-sample performance of the test via a simulation study, which also includes comparisons with alternative tests. A real data analysis illustrates the practical use of the test. (C) 2021 Elsevier Inc. All rights reserved.

Název v anglickém jazyce

Omnibus test for covariate effects in conditional copula models

Popis výsledku anglicky

Conditional copulas describe the conditional dependence and the influence that covariates have on the dependence structure between two (or more) variables. Of interest is to test the null hypothesis that the covariates have a specific effect. This paper proposes an omnibus test for testing the null hypothesis of a specified effect of the covariates. The test statistic is designed for having power against many alternatives, and can be used to test for a variety of covariate effects (no effects, linear effects, partial effects, etc.). A special case is the testing problem that the covariates do not affect the dependence structure. In this semiparametric framework the marginal distribution functions are estimated using nonparametric kernel techniques and the parametric dependence model is estimated using maximum likelihood estimation. We establish the asymptotic distribution of the test statistic under the null hypothesis, and evaluate the finite-sample performance of the test via a simulation study, which also includes comparisons with alternative tests. A real data analysis illustrates the practical use of the test. (C) 2021 Elsevier Inc. All rights reserved.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

<a href="/cs/project/GA19-00015S" target="_blank" >GA19-00015S: Identifikace schémat časového vývoje indikátorů chudoby a sociálního vyčlenění domácností založená na vícerozměrných panelových datech smíšeného typu</a><br>

Návaznosti

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

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název periodika

Journal of Multivariate Analysis

ISSN

0047-259X

e-ISSN

—

Svazek periodika

186

Číslo periodika v rámci svazku

November

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

21

Strana od-do

104804

Kód UT WoS článku

000702870700016

EID výsledku v databázi Scopus

2-s2.0-85115789014