d.) ARDL(1)
Running auxillary regressions where each explanatory variable is estimated as a
function of eth remaining explanatory variables can help detect
a.) omitted relevant variables
b.) irrelevant variables included
c.) collinearity
d.) heteroskedasiticity
If you run a LM test for heteroskedasiticity and reject the null hypothesis, what should
you conclude?
a.) at least one coefficients in the auxiliary regression is significantly different from
zero, the assumption var(yi.) = var(ei) = 2 is unlikely to be true
b.) there is no evidence of heteroskedasticity, the assumption var(yi.) = var(ei) = 2 is
most likely true
c.) there is heteroskedasticity present and it is correctly specified as tested
d.) there is heteroskedasticity, but it is not linear in the explanatory variables