# 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.