TITLE:
Parker Test for Heteroskedasticity Based on Sample Fitted Values
AUTHORS:
Jingming Jiang, Guangming Deng
KEYWORDS:
Multiple Linear Regression Model, Parker Test, Fitted Values, Heteroskedasticity Test
JOURNAL NAME:
Open Journal of Statistics,
Vol.11 No.3,
June
10,
2021
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.