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...

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... 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...

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... 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...

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... 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...

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... 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....

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... 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...

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... 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...

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... 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...

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... 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....

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... 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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