SAS/ETS 9.22 User's Guide 21

SAS/Ets User's Guide 21. Provides detailed reference material for using SAS/ETS software and guides you through the analysis and forecasting of features such as univariate and multivariate time series, cross-sectional time series, seasonal adjustments, multiequational nonlinear models, discrete choice models, limited dependent variable models, portfolio analysis, and generation of financial reports, with introductory and advanced examples for each procedure. You can also find complete information about two easy-to-use point-and-click applications: the Time Series Forecasting System, for automatic and interactive time series modeling and forecasting, and the Investment Analysis System, for time-value of money analysis of a variety of investments | 192 Chapter 7 The ARIMA Procedure Contents Overview ARIMA Procedure. 194 Getting Started ARIMA Procedure. 195 The Three Stages of ARIMA Modeling. 195 Identification Stage. 196 Estimation and Diagnostic Checking Stage. 201 Forecasting Stage . 207 Using ARIMA Procedure Statements. 209 General Notation for ARIMA Models. 210 Stationarity. 213 Differencing. 213 Subset Seasonal and Factored ARMA Models. 215 Input Variables and Regression with ARMA Errors. 216 Intervention Models and Interrupted Time Series. 219 Rational Transfer Functions and Distributed Lag Models. 221 Forecasting with Input Variables. 223 Data Requirements. 224 Syntax ARIMA Procedure. 224 Functional Summary. 225 PROC ARIMA Statement. 227 BY Statement . 231 IDENTIFY Statement. 231 ESTIMATE Statement. 235 OUTLIER Statement. 240 FORECAST Statement . 241 Details ARIMA Procedure. 243 The Inverse Autocorrelation Function. 243 The Partial Autocorrelation Function. 244 The Cross-Correlation Function. 244 The ESACF Method. 245 The MINIC Method. 246 The SCAN Method. 248 Stationarity Tests. 250 Prewhitening. 250 Identifying Transfer Function Models. 251 194 F Chapter 7 The ARIMA Procedure Missing Values and Autocorrelations. 251 Estimation Details. 252 Specifying Inputs and Transfer Functions. 256 Initial Values. 258 Stationarity and Invertibility. 259 Naming of Model Parameters. 259 Missing Values and Estimation and Forecasting. 260 Forecasting Details. 260 Forecasting Log Transformed Data. 262 Specifying Series Periodicity . 263 Detecting Outliers. 263 OUT Data Set. 265 OUTCOV Data Set. 267 OUTEST Data Set. 267 OUTMODEL SAS Data Set. 270 OUTSTAT Data Set. 272 Printed Output. 273 ODS Table Names. 275 Statistical Graphics. 277 Examples ARIMA Procedure. 280 Example Simulated IMA Model. 280 Example Seasonal Model for the Airline Series. 285 Example Model for Series J Data from Box and Jenkins . 292 Example An Intervention Model for Ozone Data. 301 Example Using Diagnostics to Identify ARIMA .