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  • 标题:Inference and model selection in general causal time series with exogenous covariates
  • 本地全文:下载
  • 作者:Mamadou Lamine Diop ; William Kengne
  • 期刊名称:Electronic Journal of Statistics
  • 印刷版ISSN:1935-7524
  • 出版年度:2022
  • 卷号:16
  • 期号:1
  • 页码:116-157
  • DOI:10.1214/21-EJS1950
  • 语种:English
  • 出版社:Institute of Mathematical Statistics
  • 摘要: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 some Lipschitz-type conditions, the existence of a τ-weakly dependent strictly stationary and ergodic solution is established. We provide conditions for the strong consistency and derive the asymptotic distribution of the quasi-maximum likelihood estimator (QMLE), both when the true parameter is an interior point of the parameters space and when it belongs to the boundary. A significance Wald-type test of parameter is developed. This test is quite extensive and includes the test of nullity of the parameter’s components, which in particular, allows us to assess the relevance of the exogenous covariates. Relying on the QMLE of the model, we also propose a penalized criterion to address the problem of the model selection for this class. The weak and the strong consistency of the procedure are established. Finally, Monte Carlo simulations are conducted to numerically illustrate the main results.
  • 关键词:60G10;60G10;62F05;62F05;62F12;62F12;62M10;62M10;Boundary;causal processes;consistency;exogenous covariates;Model selection;penalized criterion;quasi-maximum likelihood estimator;significance test
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