INTERPRETATION OF ORDER ANALYSIS

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The first part compares each model order with the next order, to see whether this next order is a significant improvement in describing the data.

We see that a model of order 3 is needed, but that order 4 is not an improvement on order 3. However, order 5 might be an improvement. 

                   Chi-square Adjustment Chi-square
                    Statistic  factor    Statistic   d.f. Prob 
Order 0 v. order 1   4998.05    0.441     2203.34     9   0.001
Order 1 v. order 2    189.21    0.434       82.09     8   0.001
Order 2 v. order 3     67.54    0.426       28.80     7   0.001
Order 3 v. order 4     11.29    0.418        4.72     6   0.580
Order 4 v. order 5     37.14    0.412       15.28     5   0.009
Order 5 v. order 6      8.87    0.404        3.58     4   0.465 
Order 6 v. order 7     11.33    0.397        4.50     3   0.212 
Order 7 v. order 8      6.30    0.392        2.47     2   0.291 
Order 8 v. order 9      1.49    0.386        0.58     1   0.448
The second part compares each order with order 9 (the maximum). Again, order 3 is called for, or possibly order 5.

                   Chi-square Adjustment  Chi-square
                    Statistic  factor     Statistic  d.f. Prob
Order 0 v. order 9   5331.23    0.421     2244.27    45   0.001
Order 1 v. order 9    333.17    0.416      138.69    36   0.001
Order 2 v. order 9    143.96    0.411       59.24    28   0.001
Order 3 v. order 9     76.42    0.407       31.08    21   0.072
Order 4 v. order 9     65.13    0.402       26.20    15   0.036
Order 5 v. order 9     28.00    0.398       11.13    10   0.347
Order 6 v. order 9     19.13    0.393        7.53     6   0.275
Order 7 v. order 9      7.79    0.390        3.04     3   0.386
Order 8 v. order 9      1.49    0.386        0.58     1   0.448
For simplicity, we will try a model of order 3.

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Antedependence Modelling 27.2.96 : Page 11 of 17