I am modelling a set of time-series, and understand various models (ARIMA, AR, GARCH) allow for the inclusion of non-Gaussian error distributions. I am aware that, after fitting a time-series model, we should perform residual analysis, checking for no correlation, mean 0 and so on.

I was wondering if, given a time-series, there was anything that suggests what error distribution to use before fitting a model? As far as I am aware, simply looking at the distribution of the time-series values is not applicable, as we are modelling errors, not the series it's self.

Packages such as 'rugarch' offer a range of error distributions - Gaussian, t, skewed-t, normal inverse Gaussian and so on, but is there any way to know which of these might best suit a data-set, without trying each of them and checking what works best?


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Browse other questions tagged or ask your own question.