SAS/ETS 9 22 User''''''''s Guide 31 ppt
SAS/ETS 9.22 User''''s Guide 31 ppt
... Approx
Parameter Estimate Error t Value Pr > |t| Lag
MU -0. 1228 0 0.1 090 2 -1.13 0.26 09 0
AR1,1 1 .97 607 0.05 499 35 .94 <.0001 1
AR1,2 -1.37 499 0. 099 67 -13.80 <.0001 2
AR1,3 0.34336 0.05502 6.24 <.0001 ... |t| Lag Variable Shift
MU 53. 3225 6 0.0 492 6 1082.51 <.0001 0 y 0
NUM1 -0.56467 0 .224 05 -2.52 0.0123 0 x 3
NUM1,1 0.42623 0.46472 0 .92 0.3 598 1 x 3
NUM1,2 0. 2...
SAS/ETS 9.22 User''''s Guide 1 ppt
... SAS Institute Inc. 2010. SAS/ETS
®
9. 22 User’s Guide.
Cary, NC: SAS Institute Inc.
SAS/ETS
®
9. 22 User’s Guide
Copyright © 2010, SAS Institute Inc., Cary, NC, USA
ISBN 97 8-1-60764-543-6
All rights ... . . . . . . . . . 28 89
V SAS/ETS Model Editor (Experimental) 292 3
Chapter 47. SAS/ETS Model Editor Window Reference . . . . . . . . . . . . 292 5
VI Investment Analysis...
SAS/ETS 9.22 User''''s Guide 3 ppt
... added to SAS/ETS software since the publication of SAS/ETS Software: Changes
and Enhancements for Release 8.2 are summarized in Chapter 1, “What’s New in SAS/ETS 9. 22. ” If
you have used SAS/ETS ... weathering (Khan 199 0)
learning curve analysis for predicting manufacturing costs of aircraft (Le Bouton 198 9)
analyzing Dow Jones stock index trends (Early, Sweeney, and Zekavat...
SAS/ETS 9.22 User''''s Guide 8 pptx
... 199 0 6 1 29. 9
2 199 0 7 130.4
3 199 0 8 131. 6
4 199 0 9 132.7
5 199 0 10 133.5
6 199 0 11 133.8
7 199 0 12 133.8
8 199 1 1 134.6
9 199 1 2 134.8
10 199 1 3 135.0
11 199 1 4 135.2
12 199 1 5 135.6
13 199 1 ... 11 231 01OCT 199 0
6 NOV 199 0 133.8 11262 01NOV 199 0
7 DEC 199 0 133.8 11 292 01DEC 199 0
8 JAN 199 1 134.6 11323 01JAN 199 1
9 FEB 199 1 134.8 11354 01FEB 199 1
10 MAR 199 1...
SAS/ETS 9.22 User''''s Guide 12 ppt
... cpidif
1 JUN 199 0 1 29. 9 . .
2 JUL 199 0 130.4 1 29. 9 0.5
3 AUG 199 0 131. 6 130.4 1.2
4 SEP 199 0 132.7 131. 6 1.1
5 OCT 199 0 133.5 132.7 0.8
6 NOV 199 0 133.8 133.5 0.3
7 DEC 199 0 133.8 133.8 0.0
8 JAN 199 1 134.6 ... JAN 199 1 134.6 133.8 0.8
9 FEB 199 1 134.8 134.6 0.2
10 MAR 199 1 135.0 134.8 0.2
11 APR 199 1 135.2 135.0 0.2
12 MAY 199 1 135.6 135.2 0.4
13 JUN 199 1 136.0 135.6 0....
SAS/ETS 9.22 User''''s Guide 13 pptx
... cpi
37 JUN 199 1 ACTUAL 0 136.000
38 JUN 199 1 FORECAST 0 136.146
39 JUN 199 1 RESIDUAL 0 -0.146
40 JUL 199 1 ACTUAL 0 136.200
41 JUL 199 1 FORECAST 0 136.566
42 JUL 199 1 RESIDUAL 0 -0.366
43 AUG 199 1 FORECAST ... Chicago JAN90 128.1 .
2 Chicago FEB90 1 29. 2 128.1
3 Chicago MAR90 1 29. 5 1 29. 2
4 Chicago APR90 130.4 1 29. 5
5 Chicago MAY90 130.4 130.4
6 Chicago JUN90 131. 7 130.4
7 Chic...
SAS/ETS 9.22 User''''s Guide 19 pptx
... restart update method of Powell ( 197 7) and Beale ( 197 2).
FR
performs the Fletcher-Reeves update (Fletcher 198 7).
PR
performs the Polak-Ribiere update (Fletcher 198 7).
CD
performs a conjugate-descent ... approximation. The trust region method is implemented using Dennis,
Gay, and Welsch ( 198 1), Gay ( 198 3), and Moré and Sorensen ( 198 3).
The trust region method performs well fo...
SAS/ETS 9.22 User''''s Guide 20 pptx
... Mei, H.H.W. ( 197 9), “Two New Unconstrained Optimization Algorithms Which
Use Function and Gradient Values,” J. Optim. Theory Appl., 28, 453–482.
Dennis, J.E. and Schnabel, R.B. ( 198 3), Numerical ... E.M.L. ( 197 2), “A Derivation of Conjugate Gradients,” in Numerical Methods for Nonlinear
Optimization, ed. F.A. Lootsma, London: Academic Press.
Dennis, J.E., Gay, D.M., and Welsch, R.E. ( 1...
SAS/ETS 9.22 User''''s Guide 33 pptx
... Technometrics, 22, 3 89 396 .
Kohn, R. and Ansley, C. ( 198 5), “Efficient Estimation and Prediction in Time Series Regression
Models,” Biometrika, 72, 3, 694 – 697 .
Ljung, G. M. and Box, G. E. P. ( 197 8), “On ... Models,” JASA, 79 (385),
84 96 .
Tsay, R. S. and Tiao, G. C. ( 198 5), “Use of Canonical Analysis in Time Series Model Identification,”
Biometrika, 72 (2), 299 315 .
Woodfield, T....
SAS/ETS 9.22 User''''s Guide 43 ppt
... Likelihood Estimates
SSE 0.2 395 4 331 DFE 79
MSE 0.00303 Root MSE 0.05507
SBC -230. 393 55 AIC -240.06 891
MAE 0.04016 596 AICC -2 39. 556 09
MAPE 0. 694 58 594 HQC -236.181 89
Durbin-Watson 1 .99 35 Regress R-Square ... 80
MSE 0.003 39 Root MSE 0.05821
SBC -226 .77848 AIC - 231. 591 92
MAE 0.04333026 AICC - 231. 44002
MAPE 153.637587 HQC -2 29. 6 593 9
Durbin-Watson 1 .92 68 Regress R...
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