SAS/Ets User's Guide 25. 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 | 232 F Chapter 7 The ARIMA Procedure CROSSCORR variable d11 d12 . d1k CROSSCORR variable d11 d12 . d1k . variable d21 d22 . d2k names the variables cross-correlated with the response variable given by the VAR specification. Each variable name can be followed by a list of differencing lags in parentheses the same as for the VAR specification. If differencing is specified for a variable in the CROSSCORR list the differenced series is cross-correlated with the VAR option series and the differenced series is used when the ESTIMATE statement INPUT option refers to the variable. DATA SAS-data-set specifies the input SAS data set that contains the time series. If the DATA option is omitted the DATA data set specified in the PROC ARIMA statement is used if the DATA option is omitted from the PROC ARIMA statement as well the most recently created data set is used. ESACF computes the extended sample autocorrelation function and uses these estimates to tentatively identify the autoregressive and moving-average orders of mixed models. The ESACF option generates two tables. The first table displays extended sample autocorrelation estimates and the second table displays probability values that can be used to test the significance of these estimates. The P pmin Pmax and Q qmin qmax options determine the size of the table. The autoregressive and moving-average orders are tentatively identified by finding a triangular pattern in which all values are insignificant. The ARIMA procedure finds these patterns based on the IDENTIFY statement ALPHA option and displays possible recommendations for the orders. The following code generates an ESACF table with dimensions of p 0 7 and q 0 8 . proc arima data test identify var x esacf p 0 7 q 0 8 run See the section The ESACF Method on page 245 for more information. MINIC uses information criteria or penalty functions to provide tentative ARMA order identification. The MINIC option generates a table that contains the computed information .
