# Detecting MA order - ARMA Modelling

I have the following problem of detecting the MA order in usage of the ACF plot.

Note: The process is stationary and the lags display monthly data.

I am not really able to say which order suits the data the best. In my opinion I could find argues for MA1 till MA12 and maybe even further.

• Consider using an AR or ARMA model instead. You will end up with a much more parsimonious model than a pure MA. Jan 3 at 18:58

With a stationary process, we can determine our $$q$$ parameter of the appropriate MA model by the number of significant lags in the ACF. The dotted-blue lines indicate the upper and lower limits for insignificant lags (based on the user-inputted confidence levels or default settings).
Some model comparison is ultimately necessary to figure out the best model due to differences in interpretation of the ACF lags. For instance, I would first compile an MA(14) model because I see the first 14 lags are each significant. Some discretion between using $$q$$ = 13 or 14 is necessary depending on how much you'd like to discriminate on significance. Many people choose to ignore the possible higher $$q$$ values suggested by higher lags that appear significant (look at the 21-27 range on your ACF plot), but it can't hurt to compare these models to the MA(14) for thoroughness. These may just be a product of some seasonality of your model.