The X12 Procedure

## Example 32.1 Model Identification

An example of the statements typically invoked when using PROC X12 for model identification might follow the same format as the following example. This example invokes the X12 procedure and uses the TRANSFORM and IDENTIFY statements. It specifies the time series data, takes the logarithm of the series (TRANSFORM statement), and generates ACFs and PACFs for the specified levels of differencing (IDENTIFY statement). The ACFs and PACFs for Nonseasonal Order=1 and Seasonal Order=1 are shown in Output 32.1.1, Output 32.1.2, Output 32.1.3, and Output 32.1.4. The data set is the same as in the section Basic Seasonal Adjustment.

The graphical displays are requested by specifying the ODS GRAPHICS statement. For more information about the graphics available in the X12 procedure, see the section ODS Graphics.

```   ods graphics on;
proc x12 data=sales date=date;
var sales;
transform power=0;
identify diff=(0,1) sdiff=(0,1);
run;
```

Output 32.1.1 ACFs (Nonseasonal Order=1 Seasonal Order=1)
The X12 Procedure

Autocorrelation of Model Residuals
Differencing: Nonseasonal Order=1 Seasonal Order=1
For variable sales
Lag Correlation Standard Error Chi-Square DF Pr > ChiSq
1 -0.34112 0.08737 15.5957 1 <.0001
2 0.10505 0.09701 17.0860 2 0.0002
3 -0.20214 0.09787 22.6478 3 <.0001
4 0.02136 0.10101 22.7104 4 0.0001
5 0.05565 0.10104 23.1387 5 0.0003
6 0.03080 0.10128 23.2709 6 0.0007
7 -0.05558 0.10135 23.7050 7 0.0013
8 -0.00076 0.10158 23.7050 8 0.0026
9 0.17637 0.10158 28.1473 9 0.0009
10 -0.07636 0.10389 28.9869 10 0.0013
11 0.06438 0.10432 29.5887 11 0.0018
12 -0.38661 0.10462 51.4728 12 <.0001
13 0.15160 0.11501 54.8664 13 <.0001
14 -0.05761 0.11653 55.3605 14 <.0001
15 0.14957 0.11674 58.7204 15 <.0001
16 -0.13894 0.11820 61.6452 16 <.0001
17 0.07048 0.11944 62.4045 17 <.0001
18 0.01563 0.11975 62.4421 18 <.0001
19 -0.01061 0.11977 62.4596 19 <.0001
20 -0.11673 0.11978 64.5984 20 <.0001
21 0.03855 0.12064 64.8338 21 <.0001
22 -0.09136 0.12074 66.1681 22 <.0001
23 0.22327 0.12126 74.2099 23 <.0001
24 -0.01842 0.12436 74.2652 24 <.0001
25 -0.10029 0.12438 75.9183 25 <.0001
26 0.04857 0.12500 76.3097 26 <.0001
27 -0.03024 0.12514 76.4629 27 <.0001
28 0.04713 0.12520 76.8387 28 <.0001
29 -0.01803 0.12533 76.8943 29 <.0001
30 -0.05107 0.12535 77.3442 30 <.0001
31 -0.05377 0.12551 77.8478 31 <.0001
32 0.19573 0.12569 84.5900 32 <.0001
33 -0.12242 0.12799 87.2543 33 <.0001
34 0.07775 0.12888 88.3401 34 <.0001
35 -0.15245 0.12924 92.5584 35 <.0001
36 -0.01000 0.13061 92.5767 36 <.0001

 Note: The P-values approximate the probability of observing a Q-value at least this large when the model fitted is correct. When DF is positive, small values of P, customarily those below 0.05 indicate model inadequacy.

Output 32.1.2 Plot for ACFs (Nonseasonal Order=1 Seasonal Order=1)

Output 32.1.3 PACFs (Nonseasonal Order=1 Seasonal Order=1)
Partial Autocorrelation of Model
Residuals
Differencing: Nonseasonal Order=1
Seasonal Order=1
For variable sales
Lag Correlation Standard Error
1 -0.34112 0.08737
2 -0.01281 0.08737
3 -0.19266 0.08737
4 -0.12503 0.08737
5 0.03309 0.08737
6 0.03468 0.08737
7 -0.06019 0.08737
8 -0.02022 0.08737
9 0.22558 0.08737
10 0.04307 0.08737
11 0.04659 0.08737
12 -0.33869 0.08737
13 -0.10918 0.08737
14 -0.07684 0.08737
15 -0.02175 0.08737
16 -0.13955 0.08737
17 0.02589 0.08737
18 0.11482 0.08737
19 -0.01316 0.08737
20 -0.16743 0.08737
21 0.13240 0.08737
22 -0.07204 0.08737
23 0.14285 0.08737
24 -0.06733 0.08737
25 -0.10267 0.08737
26 -0.01007 0.08737
27 0.04378 0.08737
28 -0.08995 0.08737
29 0.04690 0.08737
30 -0.00490 0.08737
31 -0.09638 0.08737
32 -0.01528 0.08737
33 0.01150 0.08737
34 -0.01916 0.08737
35 0.02303 0.08737
36 -0.16488 0.08737

Output 32.1.4 Plot for PACFs (Nonseasonal Order=1 Seasonal Order=1)

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