Strongly consistent model selection for general causal time series

  title={Strongly consistent model selection for general causal time series},
  author={William Kengne},
  journal={arXiv: Statistics Theory},
  • William Kengne
  • Published 20 August 2020
  • Mathematics, Computer Science
  • arXiv: Statistics Theory
Efficient and Consistent Data-Driven Model Selection for Time Series
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’.
General Hannan and Quinn criterion for common time series
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.
Some asymptotic results for time series model selection
We consider the model selection problem for a large class of time series models, including, multivariate count processes, causal processes with exogenous covariates. A procedure based on a general
Epidemic change-point detection in general causal time series
Inference and model selection in general causal time series with exogenous covariates
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


Testing for Parameter Constancy in General Causal Time‐Series Models
We consider a process belonging to a large class of causal models including AR(∞), ARCH(∞), TARCH(∞),… processes. We assume that the model depends on a parameter and consider the problem of testing
Multiple breaks detection in general causal time series using penalized quasi-likelihood
This paper is devoted to the off-line multiple breaks detection for a general class of models. The observations are supposed to fit a parametric causal process (such as classical models AR(∞),
Model identification using the Efficient Determination Criterion
On model selection from a finite family of possibly misspecified time series models
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.
Asymptotic behavior of the Laplacian quasi-maximum likelihood estimator of affine causal processes
We prove the consistency and asymptotic normality of the Laplacian Quasi-Maximum Likelihood Estimator (QMLE) for a general class of causal time series including ARMA, AR($\infty$), GARCH,
Strong consistency and asymptotic normality of the Quasi-Maximum Likelihood Estimator (QMLE) are given for a general class of multidimensional causal processes. For particular cases already studied
Model Selection Techniques: An Overview
An integrated and practically relevant discussions on theoretical properties of state-of-the-art model selection approaches are provided, in terms of their motivation, large sample performance, and applicability.
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