The X12 Procedure |
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;
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. |
Partial Autocorrelation of Model Residuals |
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---|---|---|
Differencing: Nonseasonal Order=1 Seasonal Order=1 |
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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 |
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