• Corpus ID: 31909811

SELECTION OF A MULTISTEP LINEAR PREDICTOR FOR SHORT TIME SERIES

@inproceedings{Hurvich1997SELECTIONOA,
  title={SELECTION OF A MULTISTEP LINEAR PREDICTOR FOR SHORT TIME SERIES},
  author={Clifford M. Hurvich and Chih-Ling Tsai},
  year={1997}
}
We develop a version of the Corrected Akaike Information Criterion (AICC) suitable for selection of an h-step-ahead linear predictor for a weakly sta- tionary time series in discrete time. A motivation for this criterion is provided in terms of a generalized Kullback-Leibler information which is minimized at the optimal h-step predictor, and which is equivalent to the ordinary Kullback-Leibler information when h = 1. In a simulation study, we find that if the sample size is small and the… 
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References

SHOWING 1-10 OF 22 REFERENCES
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,…
Bias of the corrected AIC criterion for underfitted regression and time series models
TLDR
A simulation study in which the true model is an infinite-order autoregression shows that, even in moderate sample sizes, AICC provides substantially better model selections than AIC.
Automatic selection of a linear predictor through frequency domain cross-validation
Given data from a weakly stationary stochastic process in discrete time, and any L-step ahead linear predictor estimated from that data, we will construct an approximately unbiased estimator of the…
PROPERTIES OF ESTIMATES OF THE MEAN SQUARE ERROR OF PREDICTION IN AUTOREGRESSIVE MODELS
Robustness of maximum likelihood estimates for multi-step predictions: The exponential smoothing case
SUMMARY We extend the argument initiated by Cox (1961) that the exponential smoothing formula can be made more robust for multi-step forecasts if the smoothing parameter is adjusted as a function of…
Order Determination for Multivariate Autoregressive Processes Using Resampling Methods
LetX1, ?, Xnbe observations from a multivariate AR(p) model with unknown orderp. A resampling procedure is proposed for estimating the orderp. The classical criteria, such as AIC and BIC, estimate…
A Study of Autoregressive and Window Spectral Estimation
SUMMARY a process with a "mixed" spectrum. The paper also includes some discussion of two different methods of estimating the coefficients of AR models (the Burg method and the Yule-Walker approach),…
The Bias of Autoregressive Coefficient Estimators
Abstract This article presents simple expressions for the bias of estimators of the coefficients of an autoregressive model of arbitrary, but known, finite order. The results include models both with…
‘Bias of some commonly-used time series estimates’
SUMMARY We study the bias of Yule-Walker and least squares estimates for univariate and multivariate autoregressive processes. We obtain explicit formulae for the large-sample bias of Yule-Walker…
Some advances in non‐linear and adaptive modelling in time‐series
TLDR
This paper shows that linear models can provide accurate forecasts provided that the parameters involved are estimated adaptively and focuses on forecasting long-memory time series analysis.
...
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