The ARIMA Procedure 
The SCAN Method 
The smallest canonical (SCAN) correlation method can tentatively identify the orders of a stationary or nonstationary ARMA process. Tsay and Tiao (1985) proposed the technique, and Box, Jenkins, and Reinsel (1994) and Choi (1992) provide useful descriptions of the algorithm.
Given a stationary or nonstationary time series with mean corrected form with a true autoregressive order of and with a true movingaverage order of , you can use the SCAN method to analyze eigenvalues of the correlation matrix of the ARMA process. The following paragraphs provide a brief description of the algorithm.
For autoregressive test order and for movingaverage test order , perform the following steps.
Let . Compute the following matrix
where ranges from to .
Find the smallest eigenvalue, , of and its corresponding normalized eigenvector, . The squared canonical correlation estimate is .
Using the as AR() coefficients, obtain the residuals for to , by following the formula: .
From the sample autocorrelations of the residuals, , approximate the standard error of the squared canonical correlation estimate by
where .
The test statistic to be used as an identification criterion is
which is asymptotically if and or if and . For and , there is more than one theoretical zero canonical correlation between and . Since the are the smallest canonical correlations for each , the percentiles of are less than those of a ; therefore, Tsay and Tiao (1985) state that it is safe to assume a . For and , no conclusions about the distribution of are made.
A SCAN table is then constructed using to determine which of the are significantly different from zero (see Table 7.7). The ARMA orders are tentatively identified by finding a (maximal) rectangular pattern in which the are insignificant for all test orders and . There may be more than one pair of values () that permit such a rectangular pattern. In this case, parsimony and the number of insignificant items in the rectangular pattern should help determine the model order. Table 7.8 depicts the theoretical pattern associated with an ARMA(2,2) series.
MA 

AR 
0 
1 
2 
3 


0 






1 






2 






3 




















MA 

AR 
0 
1 
2 
3 
4 
5 
6 
7 
0 
* 
X 
X 
X 
X 
X 
X 
X 
1 
* 
X 
X 
X 
X 
X 
X 
X 
2 
* 
X 
0 
0 
0 
0 
0 
0 
3 
* 
X 
0 
0 
0 
0 
0 
0 
4 
* 
X 
0 
0 
0 
0 
0 
0 
X = significant terms 

0 = insignificant terms 

* = no pattern 
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