Parker Test for Heteroskedasticity Based on Sample Fitted Values

Abstract

To address the drawbacks of the traditional Parker test in multivariate linear models: the process is cumbersome and computationally intensive, we propose a new heteroscedasticity test. A new heteroskedasticity test is proposed using the fitted values of the samples as new explanatory variables, reconstructing the regression model, and giving a new heteroskedasticity test based on the significance test of the coefficients, it is also compared with the existing Parker test which is improved using the principal component idea. Numerical simulations and empirical analyses show that the improved Parker test with the fitted values of the samples proposed in this paper is superior.