Test for heteroskedasticity under the assumption that the errors are independent and identically distributed (i.i.d.).
Arguments
- model
An object of class
lm.- fitted_values
Logical; if TRUE, use fitted values of regression model.
- rhs
Logical; if TRUE, specifies that tests for heteroskedasticity be performed for the right-hand-side (explanatory) variables of the fitted regression model.
- vars
Variables to be used for for heteroskedasticity test.
Value
ols_test_score returns an object of class "ols_test_score".
An object of class "ols_test_score" is a list containing the
following components:
- score
f statistic
- p
p value of
score- df
degrees of freedom
- fv
fitted values of the regression model
- rhs
names of explanatory variables of fitted regression model
- resp
response variable
- preds
predictors
References
Breusch, T. S. and Pagan, A. R. (1979) A simple test for heteroscedasticity and random coefficient variation. Econometrica 47, 1287–1294.
Cook, R. D. and Weisberg, S. (1983) Diagnostics for heteroscedasticity in regression. Biometrika 70, 1–10.
Koenker, R. 1981. A note on studentizing a test for heteroskedasticity. Journal of Econometrics 17: 107–112.
See also
Other heteroskedasticity tests:
ols_test_bartlett(),
ols_test_breusch_pagan(),
ols_test_f()
Examples
# model
model <- lm(mpg ~ disp + hp + wt, data = mtcars)
# using fitted values of the model
ols_test_score(model)
#>
#> Score Test for Heteroskedasticity
#> ---------------------------------
#> Ho: Variance is homogenous
#> Ha: Variance is not homogenous
#>
#> Variables: fitted values of mpg
#>
#> Test Summary
#> ----------------------------
#> DF = 1
#> Chi2 = 0.6452907
#> Prob > Chi2 = 0.4218014
# using predictors from the model
ols_test_score(model, rhs = TRUE)
#>
#> Score Test for Heteroskedasticity
#> ---------------------------------
#> Ho: Variance is homogenous
#> Ha: Variance is not homogenous
#>
#> Variables: disp hp wt
#>
#> Test Summary
#> ----------------------------
#> DF = 3
#> Chi2 = 0.945898
#> Prob > Chi2 = 0.8143398
# specify predictors from the model
ols_test_score(model, vars = c('disp', 'wt'))
#>
#> Score Test for Heteroskedasticity
#> ---------------------------------
#> Ho: Variance is homogenous
#> Ha: Variance is not homogenous
#>
#> Variables: disp wt
#>
#> Test Summary
#> ----------------------------
#> DF = 2
#> Chi2 = 0.5349726
#> Prob > Chi2 = 0.7653008