Perform Goldfeld-Quandt test for heteroscedasticity

Implements the Goldfeld-Quandt test on a fitted linear model with directional or two-sided alternatives.

Usage

performGQTest(model, data, order_by, fraction = 0.2,
  alternative = c("greater", "two.sided", "less"))

Details

Observations are ordered by a suspected variance-driving variable. With the split point fixed at the sample midpoint, a central fraction is omitted and the original model is re-estimated on the lower and upper segments. The statistic is the residual mean square of segment 2 divided by the residual mean square of segment 1 and is therefore directional rather than being forced above one. Split arithmetic and p-value conventions match lmtest::gqtest(..., point = 0.5).

Arguments

model

an object of class lm.

data

data frame used to fit model.

order_by

single column name used to order observations.

fraction

fraction of observations omitted from the middle; must lie strictly between zero and one.

alternative

alternative hypothesis: "greater" for increasing variance from segment 1 to segment 2, "less" for decreasing variance, or "two.sided" for either direction.

Value

An object of class htest containing the directional GQ statistic, p-value, degrees of freedom and alternative hypothesis.

References

Goldfeld, S. M., & Quandt, R. E. (1965). Some tests for homoscedasticity. Journal of the American Statistical Association, 60(310), 539–547. doi:10.1080/01621459.1965.10480811

Greene, W. H. (2018). Econometric Analysis (8th ed.). Pearson.

Examples

 data(mtcars)
 m <- lm(mpg ~ wt + qsec, data = mtcars)
 performGQTest(m, mtcars, order_by = "wt")
#> [INFO] Running Goldfeld-Quandt test
#> 
#> 	Goldfeld-Quandt test for heteroscedasticity
#> 
#> data:  mpg ~ wt + qsec
#> GQ = 0.47915, df1 = 10, df2 = 9, p-value = 0.8664
#> alternative hypothesis: variance increases from segment 1 to 2
#> 
 performGQTest(m, mtcars, order_by = "wt", alternative = "two.sided")
#> [INFO] Running Goldfeld-Quandt test
#> 
#> 	Goldfeld-Quandt test for heteroscedasticity
#> 
#> data:  mpg ~ wt + qsec
#> GQ = 0.47915, df1 = 10, df2 = 9, p-value = 0.2672
#> alternative hypothesis: variance changes from segment 1 to 2
#>