


After studying the output from Example 38.1 and identifying the ARIMA part of the model as, for example, (0 1 1)(0 1 1) 12, you can replace the IDENTIFY statement with the ARIMA and ESTIMATE statements as follows:
proc x12 data=sales date=date; var sales; transform power=0; arima model=( (0,1,1)(0,1,1) ); estimate; run ;
The parameter estimates and estimation summary statistics are shown in Output 38.2.1.
Output 38.2.1: Estimation Data
| Exact ARMA Likelihood Estimation Iteration Tolerances | |
|---|---|
| For Variable sales | |
| Maximum Total ARMA Iterations | 1500 |
| Convergence Tolerance | 1.0E-05 |
| Average absolute percentage error in within-sample forecasts: |
|
|---|---|
| For Variable sales | |
| Last year: | 2.81 |
| Last-1 year: | 6.38 |
| Last-2 year: | 7.69 |
| Last three years: | 5.63 |
| Exact ARMA Likelihood Estimation Iteration Summary | |
|---|---|
| For Variable sales | |
| Number of ARMA iterations | 6 |
| Number of Function Evaluations | 19 |
| Exact ARMA Maximum Likelihood Estimation | |||||
|---|---|---|---|---|---|
| For Variable sales | |||||
| Parameter | Lag | Estimate | Standard Error | t Value | Pr > |t| |
| Nonseasonal MA | 1 | 0.40181 | 0.07887 | 5.09 | <.0001 |
| Seasonal MA | 12 | 0.55695 | 0.07626 | 7.30 | <.0001 |
| Estimation Summary | |
|---|---|
| For Variable sales | |
| Number of Observations | 144 |
| Number of Residuals | 131 |
| Number of Parameters Estimated | 3 |
| Variance Estimate | 1.3E-03 |
| Standard Error Estimate | 3.7E-02 |
| Standard Error of Variance | 1.7E-04 |
| Log likelihood | 244.6965 |
| Transformation Adjustment | -735.2943 |
| Adjusted Log likelihood | -490.5978 |
| AIC | 987.1956 |
| AICC (F-corrected-AIC) | 987.3845 |
| Hannan Quinn | 990.7005 |
| BIC | 995.8211 |