SYMARFIMA: A dynamical model for conditionally symmetric time series with long range dependence mean structure

作     者：Benaduce, Helen da Silva Costa Pumi, Guilherme 

作者机构：Univ Fed Rio Grande Do Sul Grad Program Stat 9500 Bento Goncalves Ave BR-91509900 Porto Alegre RS Brazil 

出 版 物：《JOURNAL OF STATISTICAL PLANNING AND INFERENCE》 (统计规划与统计推断杂志)

年 卷 期：2023年第225卷

页      面：71-88页

核心收录：

学科分类：07[理学] 0714[理学-统计学（可授理学、经济学学位）] 0701[理学-数学] 070101[理学-基础数学] 

基　　金：Coordenação de Aperfeiçoamento de Pessoal de Nível Superior  CAPES 

主　　题：Dynamic models Long range dependent processes Symmetric distribution Time series analysis Generalized linear models 

摘      要：In this work we introduce a dynamical model for conditionally symmetric time series accommodating a long range dependent structure for the conditional mean. More specifically, the proposed model specify the underlying distribution of the time series, conditionally to its past, to be symmetric. The conditional mean is specified to accom-modate a long range dependent structure, following an ARFIMA-like design, as well as a (possibly time dependent) set of regressors. We provide conditions for the existence and stationarity of the proposed model as well as closed formulas for its unconditional mean, variance and covariance structure. Parameter estimation is carried out via partial likelihood. The score vector and Hessian are obtained in closed forms. A finite sample study of the proposed partial likelihood estimation is carried out. To show the usefulness of the proposed model, we present an application to a real data set related to wind speed in certain locations in Brazil. (c) 2022 Elsevier B.V. All rights reserved.