![]() | This article may be too technical for most readers to understand.(October 2014) |
In statistics, the Glejser test for heteroscedasticity, developed in 1969 by Herbert Glejser ( fr: Herbert Glejser), regresses the residuals on the explanatory variable that is thought to be related to the heteroscedastic variance. [1] After it was found not to be asymptotically valid under asymmetric disturbances, [2] similar improvements have been independently suggested by Im, [3] and Machado and Santos Silva. [4]
Step 1: Estimate original regression with ordinary least squares and find the sample residuals ei.
Step 2: Regress the absolute value |ei| on the explanatory variable that is associated with the heteroscedasticity.
Step 3: Select the equation with the highest R2 and lowest standard errors to represent heteroscedasticity.
Step 4: Perform a t-test on the equation selected from step 3 on γ1. If γ1 is statistically significant, reject the null hypothesis of homoscedasticity.
Glejser's Test can be implemented in
R software using the glejser
function of the skedastic
package.
[5] It can also be implemented in
SHAZAM econometrics software.
[6]
Breusch–Pagan test
Goldfeld–Quandt test
Park test
White test
![]() | This article may be too technical for most readers to understand.(October 2014) |
In statistics, the Glejser test for heteroscedasticity, developed in 1969 by Herbert Glejser ( fr: Herbert Glejser), regresses the residuals on the explanatory variable that is thought to be related to the heteroscedastic variance. [1] After it was found not to be asymptotically valid under asymmetric disturbances, [2] similar improvements have been independently suggested by Im, [3] and Machado and Santos Silva. [4]
Step 1: Estimate original regression with ordinary least squares and find the sample residuals ei.
Step 2: Regress the absolute value |ei| on the explanatory variable that is associated with the heteroscedasticity.
Step 3: Select the equation with the highest R2 and lowest standard errors to represent heteroscedasticity.
Step 4: Perform a t-test on the equation selected from step 3 on γ1. If γ1 is statistically significant, reject the null hypothesis of homoscedasticity.
Glejser's Test can be implemented in
R software using the glejser
function of the skedastic
package.
[5] It can also be implemented in
SHAZAM econometrics software.
[6]
Breusch–Pagan test
Goldfeld–Quandt test
Park test
White test