Modeling and Multivariate Methods - SAS

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370 Performing Time Series Analysis Chapter 14 Transfer Functions Figure 14.11 Output and Input Series Menus Diagnostics Both parts give basic diagnostics, including the sample mean (Mean), sample standard deviation (Std), and series length (N). In addition, the platform tests for stationarity using Augmented Dickey-Fuller (ADF) tests. Zero Mean ADF x t = φx t – 1 + e t Single Mean ADF x t – μ = φ ( x t – 1 – μ ) + e t Trend ADF tests against a random walk with zero mean, i.e. tests against a random walk with a non-zero mean, i.e. tests against a random walk with a non-zero mean and a linear trend, i.e. x t – μ – βt = φ[ x t – 1 – μ – β( t – 1) ] + e t Basic diagnostics also include the autocorrelation and partial autocorrelation functions, as well as the Ljung-Box Q-statistic and p-values, found under the Time Series Basic Diagnostics outline node. The Cross Correlation command adds a cross-correlation plot to the report. The length of the plot is twice that of an autocorrelation plot, or 2 × ACF length + 1 .
Chapter 14 Performing Time Series Analysis 371 Transfer Functions Figure 14.12 Cross Correlation Plot The plot includes plots of the output series versus all input series, in both numerical and graphical forms. The blue lines indicate standard errors for the statistics. Model Building Building a transfer function model is quite similar to building an ARIMA model, in that it is an iterative process of exploring, fitting, and comparing. Before building a model and during the data exploration process, it is sometimes useful to prewhiten the data. This means find an adequate model for the input series, apply the model to the output, and get residuals from both series. Compute cross-correlations from residual series and identify the proper orders for the transfer function polynomials. To prewhiten the input series, select the Prewhitening command. This brings up a dialog similar to the ARIMA dialog where you specify a stochastic model for the input series. For our SeriesJ example, we use an ARMA(2,2) prewhitening model, as shown in Figure 14.13.
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Version 10 Modeling and Multivariat
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INDIRECT OR CONSEQUENTIAL DAMAGES O
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6 Emphasis Options for Standard Lea
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8 Models with Crossed, Interaction,
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12 Examining the Residuals . . . .
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14 Response Frequencies . . . . . .
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16 19 Analyzing Principal Component
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18 Interpreting the Profiles . . .
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20 Discriminant Analysis . . . . .
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Contents Book Conventions. . . . .
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24 Learn about JMP Chapter 1 Book C
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26 Learn about JMP Chapter 1 Book C
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28 Learn about JMP Chapter 1 Additi
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Contents Launch the Fit Model Platf
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Contents Example Using Standard Lea
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56 Fitting Standard Least Squares M
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136 Fitting Stepwise Regression Mod
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158 Fitting Multiple Response Model
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Contents Overview of Generalized Li
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180 Fitting Generalized Linear Mode
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200 Performing Logistic Regression
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Contents Overview of the Screening
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232 Analyzing Screening Designs Cha
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Contents Introduction to the Nonlin
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244 Performing Nonlinear Regression
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Contents Overview of Neural Network
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280 Creating Neural Networks Chapte
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Contents Launching the Platform. .
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296 Modeling Relationships With Gau
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Contents Overview of the Loglinear
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306 Fitting Dispersion Effects with
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Contents Introduction to Partitioni
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318 Recursively Partitioning Data C
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Contents Launch the Multivariate Pl
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444 Correlations and Multivariate T
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Contents Introduction to Clustering
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462 Clustering Data Chapter 18 The
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464 Clustering Data Chapter 18 Hier
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470 Clustering Data Chapter 18 K-Me
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478 Clustering Data Chapter 18 Self
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Contents Principal Components. . .
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484 Analyzing Principal Components
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Contents Introduction . . . . . . .
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494 Performing Discriminant Analysi
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Contents Overview of the Partial Le
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Contents Introduction to Item Respo
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526 Scoring Tests Using Item Respon
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Contents Surface Plots . . . . . .
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538 Plotting Surfaces Chapter 23 La
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544 Plotting Surfaces Chapter 23 Th
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Contents Introduction to Profiling
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556 Visualizing, Optimizing, and Si
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640 References Becker, R.A. and Cle
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642 References Draper, N. and Smith
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646 References Matsumoto, M. and Ni
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648 References Rawlings, J.O. (1988
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650 References Umetrics (1995), Mul
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Contents The Response Models . . .
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654 Statistical Details Appendix A
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690 Index Birth Death Subset.jmp 46
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692 Index Ellipses Transparency 451
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694 Index test for curvature 46 Kru
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696 Index Customizing 267 Nonlinear
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698 Index reliability analysis 453-
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700 Index Stable 363 Stable Inverti
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702 Index
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Modeling and Multivariate Methods - SAS

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