Efficient and Consistent Data-Driven Model Selection for Time Series
@inproceedings{Bardet2021EfficientAC, title={Efficient and Consistent Data-Driven Model Selection for Time Series}, author={Jean-Marc Bardet and Kamila Kare and William Kengne}, year={2021} }
This paper studies the model selection problem in a large class of causal time series models, which includes both the ARMA or AR(∞) processes, as well as the GARCH or ARCH(∞), APARCH, ARMA-GARCH and many others processes. We first study the asymptotic behavior of the ideal penalty that minimizes the risk induced by a quasi-likelihood estimation among a finite family of models containing the true model. Then, we provide general conditions on the penalty term for obtaining the consistency and…
3 Citations
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References
SHOWING 1-10 OF 38 REFERENCES
Strongly consistent model selection for general causal time series
- Mathematics, Computer Science
- 2020
Order selection for same-realization predictions in autoregressive processes
- Computer Science
- 2005
This paper presents the first theoretical verification that AIC and its variants are still asymptotically elficient (in the sense defined in Section 4) for same-realization predictions, and shows that A IC also yields a satisfactory saute- realization prediction in finite samples.
ASYMPTOTIC THEORY FOR A VECTOR ARMA-GARCH MODEL
- MathematicsEconometric Theory
- 2003
This paper investigates the asymptotic theory for a vector autoregressive moving average–generalized autoregressive conditional heteroskedasticity (ARMA-GARCH) model. The conditions for the strict…
On model selection from a finite family of possibly misspecified time series models
- Computer ScienceThe Annals of Statistics
- 2019
A misspecification-resistant information criterion (MRIC) is proposed and it is shown that MRIC can be used in conjunction with a high-dimensional model selection method to select the (asymptotically) best predictive model across several high- dimensional misspecified time series models.
Regression and time series model selection in small samples
- Mathematics
- 1989
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,…
AIC, Overfitting Principles, and the Boundedness of Moments of Inverse Matrices for Vector Autotregressions and Related Models
- Mathematics
- 2002
In his somewhat informal derivation, Akaike (in "Proceedings of the 2nd International Symposium Information Theory" (C. B. Petrov and F. Csaki, Eds.), pp. 610-624, Academici Kiado, Budapest, 1973)…
AN ASYMPTOTIC THEORY FOR LINEAR MODEL SELECTION
- Mathematics
- 1997
In the problem of selecting a linear model to approximate the true un- known regression model, some necessary and/or sufficient conditions are estab- lished for the asymptotic validity of various…
Weak Dependence: With Examples and Applications
- Mathematics
- 2007
This monograph is aimed at developing Doukhan/Louhichi's (1999) idea to measure asymptotic independence of a random process. The authors propose various examples of models fitting such conditions…
Model selection and Akaike's Information Criterion (AIC): The general theory and its analytical extensions
- Mathematics
- 1987
During the last fifteen years, Akaike's entropy-based Information Criterion (AIC) has had a fundamental impact in statistical model evaluation problems. This paper studies the general theory of the…
Model selection principles in misspecified models
- Computer Science, Mathematics
- 2014
Novel asymptotic expansions of the Bayesian principle and the Kullback–Leibler divergence principle are derived in misspecified generalized linear models, which give the generalized Bayesian information criterion and generalized Akaike information criterion.