8.6 Estimation and order selection

Maximum likelihood estimation

Once the model order has been identified (i.e., the values of $p$, $d$ and $q$), we need to estimate the parameters $c$, $\phi_1,\dots,\phi_p$, $\theta_1,\dots,\theta_q$. When R estimates the ARIMA model, it uses maximum likelihood estimation (MLE). This technique finds the values of the parameters which maximize the probability of obtaining the data that we have observed. For ARIMA models, MLE is very similar to the least squares estimates that would be obtained by minimizing


(For the regression models considered in Chapters 4 and 5, MLE gives exactly the same parameter estimates as least squares estimation.) Note that ARIMA models are much more complicated to estimate than regression models, and different software will give slightly different answers as they use different methods of estimation, or different estimation algorithms.

In practice, R will report the value of the log likelihood of the data; that is, the logarithm of the probability of the observed data coming from the estimated model. For given values of $p$, $d$ and $q$, R will try to maximize the log likelihood when finding parameter estimates.

Information Criteria

Akaike’s Information Criterion (AIC), which was useful in selecting predictors for regression, is also useful for determining the order of an ARIMA model. It can be written as

$$\text{AIC} = -2 \log(L) + 2(p+q+k+1), $$

where $L$ is the likelihood of the data, $k=1$ if $c\ne0$ and $k=0$ if $c=0$. Note that the last term in parentheses is the number of parameters in the model (including $\sigma^2,$ the variance of the residuals).

For ARIMA models, the corrected AIC can be written as

$$\text{AIC}_{\text{c}} = \text{AIC} + \frac{2(p+q+k+1)(p+q+k+2)}{T-p-q-k-2}.$$

and the Bayesian Information Criterion can be written as

$$\text{BIC} = \text{AIC} + (\log(T)-2)(p+q+k+1).$$

Good models are obtained by minimizing either the AIC, AIC$_{\text{c}}$ or BIC. Our preference is to use the AIC$_{\text{c}}$.