We propose a novel polynomial network autoregressive model by incorporating higher-order connected relationships to simultaneously model the effects of both direct and indirect connections. A quasimaximum likelihood e...We propose a novel polynomial network autoregressive model by incorporating higher-order connected relationships to simultaneously model the effects of both direct and indirect connections. A quasimaximum likelihood estimation method is proposed to estimate the unknown influence parameters, and we demonstrate its consistency and asymptotic normality without imposing any distribution assumption. Moreover,an extended Bayesian information criterion is set for order selection with a divergent upper order. The application of the proposed polynomial network autoregressive model is demonstrated through both the simulation and the real data analysis.展开更多
基金国家自然科学基金面上项目“金融高频大数据下的风险推断及其与多元标的衍生品定价和金融风险管理的交叉融合研究”(71871132)国家自然科学基金委重大研究计划重点项目“金融大数据统计推断理论与方法及应用研究”(91546202)+1 种基金中央高校基本科研业务费(批准号:CXJJ-2019-412)专项资金资助部分受到上海市数据科技与决策前沿科学研究基地(Shanghai Research Center for Data Science and Decision Technology)资助。
基金supported by the Fundamental Research Funds for the Central Universities(Grant No.JBK2207075)The second author was supported by National Natural Science Foundation of China(Grant Nos.71991472,12171395,11931014 and 71532001)+1 种基金the Joint Lab of Data Science and Business Intelligence at Southwestern University of Finance and Economics and the Fundamental Research Funds for the Central Universities(Grant No.JBK1806002)The fourth author was supported by the Humanity and Social Science Youth Foundation of Ministry of Education of China(Grant No.19YJC790204)。
文摘We propose a novel polynomial network autoregressive model by incorporating higher-order connected relationships to simultaneously model the effects of both direct and indirect connections. A quasimaximum likelihood estimation method is proposed to estimate the unknown influence parameters, and we demonstrate its consistency and asymptotic normality without imposing any distribution assumption. Moreover,an extended Bayesian information criterion is set for order selection with a divergent upper order. The application of the proposed polynomial network autoregressive model is demonstrated through both the simulation and the real data analysis.
基金Partially supported by a grant from the"Project 211"of the Central University of Finance and Economics,the CUFE Young Scholar Innovation Fund and NSFC(No.11101448)
