The ARIMA Procedure
- Overview
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Getting Started
The Three Stages of ARIMA ModelingIdentification StageEstimation and Diagnostic Checking StageForecasting StageUsing ARIMA Procedure StatementsGeneral Notation for ARIMA ModelsStationarityDifferencingSubset, Seasonal, and Factored ARMA ModelsInput Variables and Regression with ARMA ErrorsIntervention Models and Interrupted Time SeriesRational Transfer Functions and Distributed Lag ModelsForecasting with Input VariablesData Requirements -
Syntax
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DetailsThe Inverse Autocorrelation FunctionThe Partial Autocorrelation FunctionThe Cross-Correlation FunctionThe ESACF MethodThe MINIC MethodThe SCAN MethodStationarity TestsPrewhiteningIdentifying Transfer Function ModelsMissing Values and AutocorrelationsEstimation DetailsSpecifying Inputs and Transfer FunctionsInitial ValuesStationarity and InvertibilityNaming of Model ParametersMissing Values and Estimation and ForecastingForecasting DetailsForecasting Log Transformed DataSpecifying Series PeriodicityDetecting OutliersOUT= Data SetOUTCOV= Data SetOUTEST= Data SetOUTMODEL= SAS Data SetOUTSTAT= Data SetPrinted OutputODS Table NamesStatistical Graphics
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Examples
- References
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:
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the BY variables.
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_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).
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_TYPE_, a character variable that contains the estimation method used. _TYPE_ can have the value CLS, ULS, or ML.
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_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.
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_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
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Akaike’s information criterion
- SBC
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Schwarz’s Bayesian criterion
- LOGLIK
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the log-likelihood, if METHOD=ML or METHOD=ULS is specified
- SSE
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the sum of the squared residuals
- NUMRESID
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the number of residuals
- NPARMS
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the number of parameters in the model
- NDIFS
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the sum of the differencing lags employed for the response variable
- ERRORVAR
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the estimate of the innovation variance
- MU
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the estimate of the mean term
- CONV
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tells if the estimation converged. The value of 0 signifies that estimation converged. Nonzero values reflect convergence problems.
- NITER
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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:
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CONV= 0 indicates that the estimation process has converged.
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CONV= 1 or 2 indicates that the estimation process has run into numerical problems (such as encountering an unstable model or a ridge) during the iterations.
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CONV= 3 or greater indicates that the estimation process has failed to converge.