Re: [R] Function stslshac {sphet}: heteroskedasticity and autocorrelation consistent (HAC) estimator

data: AucklandHAC$residuals weights: auckland.listw Moran I statistic standard deviate = -4.302, p-value = 1.693e-05 alternative hypothesis: two.sided sample estimates: Moran I statistic Expectation Variance -0.230081673 -0.006024096 0.002712539 On 19 September 2015 a

[R] Function stslshac {sphet}: heteroskedasticity and autocorrelation consistent (HAC) estimator

Dear R-users, I have quite basic question for econometricians, however I would like to be sure in this. If I use a HAC estimator of the variance-covariance (VC) matrix for a spatial econometric model, do I still need to test the residuals for spatial autocorrelation and heteroscedasticity? (in pa

[R] Prediction Intervals predict.Arima

I would like to ask how exactly the prediction intervals are calculated by function predict.arima in R. I suppose that the method is same as for the function forecast (which I am actually using). Unfortunately I can not find it anywhere. I am particularly interested in how it works for Arima models