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
| OUTSTAT= Data Set |
PROC ARIMA writes the diagnostic statistics for a model to an output data set when the OUTSTAT= option is specified in the ESTIMATE statement. The OUTSTAT data set contains the following:
the BY variables.
_MODLABEL_, a character variable that contains the model label, if it is provided by using the label option in the ESTIMATE statement (otherwise this variable is not created).
_TYPE_, a character variable that contains the estimation method used. _TYPE_ can have the value CLS, ULS, or ML.
_STAT_, a character variable that contains the name of the statistic given by the _VALUE_ variable in this observation. _STAT_ takes on the values AIC, SBC, LOGLIK, SSE, NUMRESID, NPARMS, NDIFS, ERRORVAR, MU, CONV, and NITER.
_VALUE_, a numeric variable that contains the value of the statistic named by the _STAT_ variable.
The observations contained in the OUTSTAT= data set are identified by the _STAT_ variable. A description of the values of the _STAT_ variable follows:
- AIC
Akaike’s information criterion
- SBC
Schwarz’s Bayesian criterion
- LOGLIK
the log-likelihood, if METHOD=ML or METHOD=ULS is specified
- SSE
the sum of the squared residuals
- NUMRESID
the number of residuals
- NPARMS
the number of parameters in the model
- NDIFS
the sum of the differencing lags employed for the response variable
- ERRORVAR
the estimate of the innovation variance
- MU
the estimate of the mean term
- CONV
tells if the estimation converged. The value of 0 signifies that estimation converged. Nonzero values reflect convergence problems.
- NITER
the number of iterations
Remark. CONV takes an integer value that corresponds to the error condition of the parameter estimation process. The value of 0 signifies that estimation process has converged. The higher values signify convergence problems of increasing severity. Specifically:
CONV
indicates that the estimation process has converged. CONV
or 
indicates that the estimation process has run into numerical problems (such as encountering an unstable model or a ridge) during the iterations. CONV
indicates that the estimation process has failed to converge.