If I want to fit an ARMA on data whose white noise part is non-normal is it better to estimate the model with OLS or with MLE? In other words, is OLS or MLE better for non-normal white noise in time series?
ARMA – Fitting a Non-Normal ARMA Process with OLS or MLE
armaleast squaresmaximum likelihoodnormality-assumption
Best Answer
ARMA($p,q$) with $q>0$
ARMA($p,q$) model cannot be estimated by OLS if $q>0$, because the design matrix cannot be constructed (it includes lagged errors which are unobservable). However, MLE or conditional MLE are feasible options.
ARMA($p,q$) with $q=0$
When $q=0$, both OLS and (conditional or unconditional) MLE are feasible.
OLS estimation will be equivalent to conditional MLE with the assumption that the errors are normally distributed.
For nonnormal error distributions, OLS will be consistent, and
Summary
MLE or conditional MLE could be preferred to OLS when the error distribution can be guessed with good accuracy as then MLE would be more efficient. Otherwise OLS seems safer.