The final estimate for
, where p is the AR order, AIC is the value of Akaike's Information Criterion, and L is the likelihood function evaluated for the optimum autoregressive model.
A double scalar equal to the estimate for the
.
If MaximumLikelihood is chosen as the EstimationMethod, the exact likelihood evaluated for the optimum autoregressive model will be returned instead. Otherwise it is calculated using the approximation to the AIC.
| Exception Type | Condition |
|---|---|
| MatrixSingularException | is thrown if the input matrix is singular. |
| TooManyCallsException | is thrown if the number of calls to the function has exceeded the maximum number of iterations times the number of moving average (MA) parameters + 1. |
| IncreaseErrRelException | is thrown if the bound for the relative error is too small. |
| NewInitialGuessException | is thrown if the iteration has not made good progress. |
| IllConditionedException | is thrown if the problem is ill-conditioned. |
| TooManyIterationsException | is thrown if the maximum number of iterations is exceeded. |
| TooManyFunctionEvaluationsException | is thrown if the maximum number of function evaluations is exceeded. |
| TooManyJacobianEvalException | is thrown if the maximum number of Jacobian evaluations is exceeded. |
| SingularTriangularMatrixException | is thrown if the input triangular matrix is singular. |
| NonStationaryException | is thrown if the final maximum likelihood estimates for the time series are nonstationary. |
ARAutoUnivariate Class | Imsl.Stat Namespace