G13DBF Example Program Results Number of valid parameters = 3 Multivariate partial autocorrelations 0.64498 0.92669 0.84300 Zero lag predictor error variance determinant followed by error variance ratios 0.00000 0.35502 0.02603 0.00409 Prediction error variances Lag = 1 0.00811 -0.00511 0.00159 -0.00029 -0.00511 0.04089 0.00757 0.01843 0.00159 0.00757 0.03834 -0.01894 -0.00029 0.01843 -0.01894 0.06760 Lag = 2 0.00354 -0.00087 -0.00075 -0.00105 -0.00087 0.01946 0.00535 0.00566 -0.00075 0.00535 0.01900 -0.01071 -0.00105 0.00566 -0.01071 0.04058 Lag = 3 0.00301 -0.00087 -0.00054 0.00065 -0.00087 0.01824 0.00872 0.00247 -0.00054 0.00872 0.00935 -0.00216 0.00065 0.00247 -0.00216 0.02254 Last backward prediction error variances Lag = 3 0.00331 -0.00392 -0.00106 0.00592 -0.00392 0.01890 0.00348 -0.00330 -0.00106 0.00348 0.01003 -0.01054 0.00592 -0.00330 -0.01054 0.03336 Prediction coefficients Lag = 1 0.81861 0.23399 -0.17097 0.09256 0.06738 -0.48720 -0.14064 0.04295 0.15036 0.11924 -0.36725 -0.42092 -0.70971 0.02998 0.59779 0.34610 Lag = 2 -0.34049 -0.13370 0.40610 -0.02183 -1.27574 -0.13591 -0.65779 -0.11267 -0.45439 0.19379 0.63420 0.33920 -0.43237 -0.54848 -0.62897 0.16670 Lag = 3 0.16437 0.13858 0.01290 0.03463 0.39291 0.07407 -0.08802 -0.15361 -1.29240 -0.24489 0.30235 0.39442 0.89768 -0.39040 0.25151 -0.28304 Backward prediction coefficients Lag = 1 0.41541 0.06149 0.15319 0.05079 0.12370 -0.26471 -0.22721 0.48503 -0.86933 -0.47373 0.37924 0.13814 1.30779 -0.09178 -1.45398 -0.21967 Lag = 2 -0.06740 -0.12255 -0.13673 -0.09730 -1.24801 0.03090 0.51706 -0.28925 0.98045 -0.20194 0.16307 -0.10869 -1.68389 -0.74589 0.52900 0.41580 Lag = 3 0.03794 0.10491 -0.21635 0.08015 0.75392 0.22603 -0.25661 -0.47450 -0.00338 0.05636 -0.08818 0.12723 0.55022 -0.41232 0.71649 -0.14565