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Richard Hardy
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Applying AIC to determine appropriate ARIMA model

If I somehow know that a variable $Y$ is explained by an ARIMA process, and I know the number of times that the observations must be "differenced" to obtain a stationary series, I have read that it is appropriate to use AIC to determine the number of regressor variables in the model.

The formula for AIC is $AIC=2p + 2ln(L)$, where $p$ is the total number of regressors, and $L$ is the maximum of the likelihood function, and the best number of regressors is the one which minimizes the AIC.

My question is, if I determine for example that AIC is lowest when $p=5$, how do I know which of these five regressors are lagged $y$ variables, and which of the five regressors are lagged error terms?