The ARIMA Procedure
- Overview
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Getting Started
The Three Stages of ARIMA Modeling Identification Stage Estimation and Diagnostic Checking Stage Forecasting Stage Using ARIMA Procedure Statements General Notation for ARIMA Models Stationarity Differencing Subset, Seasonal, and Factored ARMA Models Input Variables and Regression with ARMA Errors Intervention Models and Interrupted Time Series Rational Transfer Functions and Distributed Lag Models Forecasting with Input Variables Data Requirements -
Syntax
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Details
The Inverse Autocorrelation Function The Partial Autocorrelation Function The Cross-Correlation Function The ESACF Method The MINIC Method The SCAN Method Stationarity Tests Prewhitening Identifying Transfer Function Models Missing Values and Autocorrelations Estimation Details Specifying Inputs and Transfer Functions Initial Values Stationarity and Invertibility Naming of Model Parameters Missing Values and Estimation and Forecasting Forecasting Details Forecasting Log Transformed Data Specifying Series Periodicity Detecting Outliers OUT= Data Set OUTCOV= Data Set OUTEST= Data Set OUTMODEL= SAS Data Set OUTSTAT= Data Set Printed Output ODS Table Names Statistical Graphics
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Examples
- References
| OUTLIER Statement |
The OUTLIER statement can be used to detect shifts in the level of the response series that are not accounted for by the previously estimated model. An ESTIMATE statement must precede the OUTLIER statement. The following options are used in the OUTLIER statement:
- TYPE=ADDITIVE
- TYPE=SHIFT
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TYPE=TEMP (
)
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TYPE=(<ADDITIVE><SHIFT> <TEMP ( ) )>
specifies the types of level shifts to search for. The default is TYPE=(ADDITIVE SHIFT), which requests searching for additive outliers and permanent level shifts. The option
TEMP() requests searching for temporary changes in the level of durations . These options can also be abbreviated as AO, LS, and TC. - ALPHA=significance-level
specifies the significance level for tests in the OUTLIER statement. The default is 0.05.
- SIGMA=ROBUST | MSE
specifies the type of error variance estimate to use in the statistical tests performed during the outlier detection. SIGMA=MSE corresponds to the usual mean squared error (MSE) estimate, and SIGMA=ROBUST corresponds to a robust estimate of the error variance. The default is SIGMA=ROBUST.
- MAXNUM=number
limits the number of outliers to search. The default is MAXNUM=5.
- MAXPCT=number
limits the number of outliers to search for according to a percentage of the series length. The default is MAXPCT=2. When both the MAXNUM= and MAXPCT= options are specified, the minimum of the two search numbers is used.
- ID=Date-Time ID variable
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specifies a SAS date, time, or datetime identification variable to label the detected outliers. This variable must be present in the input data set.
The following examples illustrate a few possibilities for the OUTLIER statement.
The most basic usage, shown as follows, sets all the options to their default values.
outlier;That is, it is equivalent to
outlier type=(ao ls) alpha=0.05 sigma=robust maxnum=5 maxpct=2;The following statement requests a search for permanent level shifts and for temporary level changes of durations 6 and 12. The search is limited to at most three changes and the significance level of the underlying tests is 0.001. MSE is used as the estimate of error variance. It also requests labeling of the detected shifts using an ID variable date.
outlier type=(ls tc(6 12)) alpha=0.001 sigma=mse maxnum=3 ID=date;