• Corpus ID: 54684871

ASYMPTOTIC EFFICIENCY OF THE ORDER SELECTION OF A NONGAUSSIAN AR PROCESS

@inproceedings{Karagrigoriou1997ASYMPTOTICEO,
  title={ASYMPTOTIC EFFICIENCY OF THE ORDER SELECTION OF A NONGAUSSIAN AR PROCESS},
  author={Alex Karagrigoriou},
  year={1997}
}
Motivated by Shibata's (1980) asymptotic efficiency results for the order selected for a zero mean Gaussian AR process this paper establishes the asymp- totic efficiency of AIC-like model selection criteria for infinite order autoregressive processes with zero mean and unobservable errors that constitute a sequence of nongaussian random variables. Furthermore, from the spectral density point of view, the asympotic efficiency of AIC-like information criteria is established when the underlying… 
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References

SHOWING 1-10 OF 24 REFERENCES
Asymptotic efficiency of model selection criteria: the nonzero mean Gaussian AR(∞) case
Motivated by Shibata’s (1980) asymptotic efficiency results this paper dis-cusses the asymptotic efficiency of the order selected by a selection procedure for an infinite order autoregressive process
Selection of the order of an autoregressive model by Akaike's information criterion
SUMMARY The asymptotic distribution is obtained of the order of regression selected by Akaike's information criterion in autoregressive models. The asymptotic quadratic risks of estimates of
Regression and time series model selection in small samples
SUMMARY A bias correction to the Akaike information criterion, AIC, is derived for regression and autoregressive time series models. The correction is of particular use when the sample size is small,
THE CRITERION AUTOREGRESSIVE TRANSFER FUNCTION OF PARZEN
. A mathematical derivation of the Criterion Autoregressive Transfer Function (CAT) of Parzen (1974) is given and a generalization of this criterion is introduced. The asymptotic distribution of the
On selection of the order of the spectral density model for a stationary process
SummaryLet {X(t)} be a stationary process with mean zero and spectral densityg(x). We shall use akth order parametric spectral modelfτ(k)(x) for this process. Without Gaussianity we can obtain an
Some properties of the order of an autoregressive model selected by a generalization of Akaike∘s EPF criterion
SUMMARY The asymptotic distribution of the order of an autoregression selected by a generalization of Akaike's FPE criterion is given. Some of the properties of the distribution are investigated. The
REGRESSION, AUTOREGRESSION MODELS
. The accuracy of least squares fitted regression autoregression models as approximations to more general stochastic structures is considered, attention being paid to the accuracy of the estimates of
Asymptotically Efficient Selection of the Order by the Criterion Autoregressive Transfer Function
On montre que la propriete d'optimalite obtenue par Shibata (1980, 1981) est valable pour l'approche CAT (Parzen, 1974), CAT * (Parzen, 1977) et CAT 2 (Bhansali, 1985)
The determination of the order of an autoregression
SUMMARY It is shown that a strongly consistent estimation procedure for the order of an autoregression can be based on the law of the iterated logarithm for the partial autocorrelations. As compared
Some recent advances in time series modeling
The aim of this paper is to describe some of the important concepts and techniques which seem to help provide a solution of the stationary time series problem (prediction and model identification).
...
1
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3
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