 |
|
| The ARIMA Procedure |
| Functional Summary |
The statements and options that control the ARIMA procedure are summarized in Table 7.3.
Description |
Statement |
Option |
|---|---|---|
Data Set Options |
||
specify the input data set |
PROC ARIMA |
DATA= |
IDENTIFY |
DATA= |
|
specify the output data set |
PROC ARIMA |
OUT= |
FORECAST |
OUT= |
|
include only forecasts in the output data set |
FORECAST |
NOOUTALL |
write autocovariances to output data set |
IDENTIFY |
OUTCOV= |
write parameter estimates to an output data set |
ESTIMATE |
OUTEST= |
write correlation of parameter estimates |
ESTIMATE |
OUTCORR |
write covariance of parameter estimates |
ESTIMATE |
OUTCOV |
write estimated model to an output data set |
ESTIMATE |
OUTMODEL= |
write statistics of fit to an output data set |
ESTIMATE |
OUTSTAT= |
Options for Identifying the Series |
||
difference time series and plot autocorrelations |
IDENTIFY |
|
specify response series and differencing |
IDENTIFY |
VAR= |
specify and cross-correlate input series |
IDENTIFY |
CROSSCORR= |
center data by subtracting the mean |
IDENTIFY |
CENTER |
exclude missing values |
IDENTIFY |
NOMISS |
delete previous models and start |
IDENTIFY |
CLEAR |
specify the significance level for tests |
IDENTIFY |
ALPHA= |
perform tentative ARMA order identification by using the ESACF method |
IDENTIFY |
ESACF |
perform tentative ARMA order identification by using the MINIC method |
IDENTIFY |
MINIC |
perform tentative ARMA order identification by using the SCAN method |
IDENTIFY |
SCAN |
specify the range of autoregressive model orders for estimating the error series for the MINIC method |
IDENTIFY |
PERROR= |
determine the AR dimension of the SCAN, ESACF, and MINIC tables |
IDENTIFY |
P= |
determine the MA dimension of the SCAN, ESACF, and MINIC tables |
IDENTIFY |
Q= |
perform stationarity tests |
IDENTIFY |
STATIONARITY= |
selection of white noise test statistic in the presence of missing values |
IDENTIFY |
WHITENOISE= |
Options for Defining and Estimating the Model |
||
specify and estimate ARIMA models |
ESTIMATE |
|
specify autoregressive part of model |
ESTIMATE |
P= |
specify moving-average part of model |
ESTIMATE |
Q= |
specify input variables and transfer functions |
ESTIMATE |
INPUT= |
drop mean term from the model |
ESTIMATE |
NOINT |
specify the estimation method |
ESTIMATE |
METHOD= |
use alternative form for transfer functions |
ESTIMATE |
ALTPARM |
suppress degrees-of-freedom correction in variance estimates |
ESTIMATE |
NODF |
selection of white noise test statistic in the presence of missing values |
ESTIMATE |
WHITENOISE= |
Options for Outlier Detection |
||
specify the significance level for tests |
OUTLIER |
ALPHA= |
identify detected outliers with variable |
OUTLIER |
ID= |
limit the number of outliers |
OUTLIER |
MAXNUM= |
limit the number of outliers to a percentage of the series |
OUTLIER |
MAXPCT= |
specify the variance estimator used for testing |
OUTLIER |
SIGMA= |
specify the type of level shifts |
OUTLIER |
TYPE= |
Printing Control Options |
||
limit number of lags shown in correlation plots |
IDENTIFY |
NLAG= |
suppress printed output for identification |
IDENTIFY |
NOPRINT |
plot autocorrelation functions of the residuals |
ESTIMATE |
PLOT |
print log-likelihood around the estimates |
ESTIMATE |
GRID |
control spacing for GRID option |
ESTIMATE |
GRIDVAL= |
print details of the iterative estimation process |
ESTIMATE |
PRINTALL |
suppress printed output for estimation |
ESTIMATE |
NOPRINT |
suppress printing of the forecast values |
FORECAST |
NOPRINT |
print the one-step forecasts and residuals |
FORECAST |
PRINTALL |
Plotting Control Options |
||
request plots associated with model identification, residual analysis, and forecasting |
PROC ARIMA |
PLOTS= |
Options to Specify Parameter Values |
||
specify autoregressive starting values |
ESTIMATE |
AR= |
specify moving-average starting values |
ESTIMATE |
MA= |
specify a starting value for the mean parameter |
ESTIMATE |
MU= |
specify starting values for transfer functions |
ESTIMATE |
INITVAL= |
Options to Control the Iterative Estimation Process |
||
specify convergence criterion |
ESTIMATE |
CONVERGE= |
specify the maximum number of iterations |
ESTIMATE |
MAXITER= |
specify criterion for checking for singularity |
ESTIMATE |
SINGULAR= |
suppress the iterative estimation process |
ESTIMATE |
NOEST |
omit initial observations from objective |
ESTIMATE |
BACKLIM= |
specify perturbation for numerical derivatives |
ESTIMATE |
DELTA= |
omit stationarity and invertibility checks |
ESTIMATE |
NOSTABLE |
use preliminary estimates as starting values for ML and ULS |
ESTIMATE |
NOLS |
Options for Forecasting |
||
forecast the response series |
FORECAST |
|
specify how many periods to forecast |
FORECAST |
LEAD= |
specify the ID variable |
FORECAST |
ID= |
specify the periodicity of the series |
FORECAST |
INTERVAL= |
specify size of forecast confidence limits |
FORECAST |
ALPHA= |
start forecasting before end of the input data |
FORECAST |
BACK= |
specify the variance term used to compute forecast standard errors and confidence limits |
FORECAST |
SIGSQ= |
control the alignment of SAS date values |
FORECAST |
ALIGN= |
BY Groups |
||
specify BY group processing |
BY |
|
|
|
Copyright © 2008 by SAS Institute Inc., Cary, NC, USA. All rights reserved.