Testing for heteroscedasticity in regression models

Autor:	William Holt, Maria Carapeto
Rok vydání:	2003
Předmět:	Statistics and Probability Heteroscedasticity Artificial neural network Monte Carlo method Statistics Econometrics Range (statistics) Regression analysis Statistics Probability and Uncertainty Park test Mathematics Test (assessment)
Zdroj:	Journal of Applied Statistics. 30:13-20
ISSN:	1360-0532 0266-4763
DOI:	10.1080/0266476022000018475
Popis:	A new test for heteroscedasticity in regression models is presented based on the Goldfeld-Quandt methodology. Its appeal derives from the fact that no further regressions are required, enabling widespread use across all types of regression models. The distribution of the test is computed using the Imhof method and its power is assessed by performing a Monte Carlo simulation. We compare our results with those of Griffiths & Surekha (1986) and show that our test is more powerful than the wide range of tests they examined. We introduce an estimation procedure using a neural network to correct the heteroscedastic disturbances.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::d5466610ba04eb7ad82433aaab8b6e9d https://doi.org/10.1080/0266476022000018475 Zobrazit plný text záznamu Plný text ve formátu PDF