Robust testing for normality of error terms with presence of autocorrelation and conditional heteroscedasticity

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43110%2F17%3A43910795" target="_blank" >RIV/62156489:43110/17:43910795 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1063/1.4972747" target="_blank" >http://dx.doi.org/10.1063/1.4972747</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1063/1.4972747" target="_blank" >10.1063/1.4972747</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Robust testing for normality of error terms with presence of autocorrelation and conditional heteroscedasticity

Popis výsledku v původním jazyce

Normality of the error terms in regression models is one of the basic assumptions in the applied regression analysis. Therefore, testing for normality of the error terms constitutes one of the most important steps of regression model verification and validation. Failure to assess non-normality of the error terms may lead to incorrect results of usual statistical inference techniques such as t-test or F-test. Within the applied regression analysis there is a frequent problem of the presence of autocorrelation and conditional heteroscedasticity of the error terms. Under both autocorrelation and heteroscedasticity, the usual OLS estimators are still unbiased, linear and asymptotically normally distributed, however, no longer have the minimum variance property among all linear unbiased estimators. Therefore, the aim of this paper is to present and discuss normality testing of the error terms with presence of autocorrelation and conditional heteroscedasticity. To explore the power of selected classical tests and robust tests for normality, we perform simulation study.

Název v anglickém jazyce

Robust testing for normality of error terms with presence of autocorrelation and conditional heteroscedasticity

Popis výsledku anglicky

Normality of the error terms in regression models is one of the basic assumptions in the applied regression analysis. Therefore, testing for normality of the error terms constitutes one of the most important steps of regression model verification and validation. Failure to assess non-normality of the error terms may lead to incorrect results of usual statistical inference techniques such as t-test or F-test. Within the applied regression analysis there is a frequent problem of the presence of autocorrelation and conditional heteroscedasticity of the error terms. Under both autocorrelation and heteroscedasticity, the usual OLS estimators are still unbiased, linear and asymptotically normally distributed, however, no longer have the minimum variance property among all linear unbiased estimators. Therefore, the aim of this paper is to present and discuss normality testing of the error terms with presence of autocorrelation and conditional heteroscedasticity. To explore the power of selected classical tests and robust tests for normality, we perform simulation study.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

<a href="/cs/project/GA16-07089S" target="_blank" >GA16-07089S: Robustní přístup testování normality chybového členu v ekonometrických modelech</a><br>

Návaznosti

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

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název statě ve sborníku

AIP Conference Proceedings 1798

ISBN

978-0-7354-1464-8

ISSN

—

e-ISSN

neuvedeno

Počet stran výsledku

6

Strana od-do

&quot;Nestránkováno&quot;

Název nakladatele

American Institute of Physics (AIP)

Místo vydání

Melville

Místo konání akce

La Rochelle

Datum konání akce

4. 7. 2016

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000399203000154