Testing for heteroscedasticity in regression models

Abstract

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.