HANDLING HETEROSCEDASTICITY IN LINEAR MODELS: HUBER-WHITE STANDARD ERRORS VS BOOTSTRAP CONFIDENCE INTERVALS

Autor:	Zarrukh Rakhimov
Jazyk:	angličtina
Rok vydání:	2024
Předmět:	heteroscedasticity homosce-dasticity linear model confidence interval bootstrap huber-white residuals accuracy Economics as a science HB71-74
Zdroj:	Zbornik Radova Ekonomskog Fakulteta u Istočnom Sarajevu, Iss 29, Pp 11-22 (2024)
Druh dokumentu:	article
ISSN:	1840-3557 1986-6690
DOI:	10.7251/ZREFIS2429011R
Popis:	Heteroscedasticity are one of the several violations of the assumptions of OLS. If no remedy applied, residuals with non-constant variance can lead to inaccurate and biased results. Academia has suggested a wide range of remedies to tackle with heteroscedastic residuals. In this study, we suggest another approach, bootstrapping of dataset to construct our confidence intervals. In order to compare the outcome, we look at Huber-White method and look at its performance against bootstrap intervals. Results indicate that bootstrap intervals perform equally well as Huber-White based confidence intervals. This indicates that bootstrap method, similar to Huber-White approach, can be a good remedy for heteroscedasticity.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/c9ec7dbaff7c4aab8f57b6f46f3b9014 Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF