# Solved – Testing heteroscedasticity in seasonal ARIMA model

arimaheteroscedasticityr

I have estimated seasonal ARIMA(1,2,1)x(0,0,2) and then to test for heteroscedasicity transformed it to lm object with

x <- lm(residuals(m) ~ 1), with m = auto.arima(ts.loggdpq,stepwise=FALSE)
and ts.loggdpq is quaterrly, logged gdp data.

Testing for heteroscedasicity with bptest(x) resulted with

        studentized Breusch-Pagan test

data:  x BP = 3.429e-30, df = 0, p-value < 2.2e-16


meaning that I can't reject the null hypothesis. Does this mean that my model is not effective? Should I transform it to ARCH(q) model?

Your $p$-value is very small, so you actually will reject the null hypothesis at any sensible significance level.
(I just wonder if you are using the test correctly. Are you supplying any explanatory variables for the test? Why do you have zero degrees of freedom there: df=0?)