# Strongly consistent model selection for general causal time series

@article{Kengne2020StronglyCM,
title={Strongly consistent model selection for general causal time series},
author={William Kengne},
journal={arXiv: Statistics Theory},
year={2020}
}
• William Kengne
• Published 20 August 2020
• Mathematics, Computer Science
• arXiv: Statistics Theory
6 Citations
Efficient and Consistent Data-Driven Model Selection for Time Series
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• 2021
This paper proves that consistent model selection criteria outperform classical AIC criterion in terms of efficiency and derives from a Bayesian approach the usual BIC criterion, by keeping all the second order terms of the Laplace approximation, a data-driven criterion denoted KC’.
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This paper aims to study data driven model selection criteria for a large class of time series, which includes ARMA or AR(∞) processes, as well as GARCH or ARCH(∞), APARCH and many others processes.
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• Mathematics
Statistics & Probability Letters
• 2022
Inference and model selection in general causal time series with exogenous covariates
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Electronic Journal of Statistics
• 2022
In this paper, we study a general class of causal processes with exogenous covariates, including many classical processes such as the ARMA-GARCH, APARCH, ARMAX, GARCH-X and APARCH-X processes. Under

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The Estimation of the Order of an ARMA Process
>2LK(j)E(n -j), Y,0?K(j)zi = k(z) = gh, where the K(j) decrease to zero at a geometric rate, and that the E(n) are the linear innovations. It has been assumed that &{x(n)} = 0 but this is immaterial