I am conducting some time series forecasts using quite limited data, 13 years annually. Basically, I am trying to forecast companies emission totals using historical values. The historical data however, seems to be nothing but white noise, e.g no significant lags on ACF, however Box Ljung <0.05.
My question is if it would be helpful to include correlated variables in a ARIMA model, with xreg..? For example including GDP, World Emissions, and Energy Prices. Would this improve the forecasts at all, or would any improvement on the test accuracy only be coincidences (limited data).