矩阵型截面数据时间序列自回归模型

吴述金, 华楠

数学学报 ›› 2022, Vol. 65 ›› Issue (6) : 1093-1104.

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中国科学院数学与系统科学研究院期刊网
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数学学报 ›› 2022, Vol. 65 ›› Issue (6) : 1093-1104. DOI: 10.12386/A20190057
论文

矩阵型截面数据时间序列自回归模型

    吴述金, 华楠
作者信息 +

Autoregression Model of Time Series with Matrix Cross-Section Data

    Shu Jin WU, Nan HUA
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文章历史 +

摘要

矩阵型截面数据时间序列的优点在于可以同时刻画多个对象的多个属性.本文重点研究了矩阵型截面数据时间序列的自回归模型，给出了该模型的参数估计、模型识别、白噪声检验三个方面的理论结果.最后再利用矩阵型截面数据时间序列自回归模型，对两支银行股的日收益率序列和日成交量变化率序列进行建模分析.

Abstract

The advantage of time series with matrix cross-section data is that multiple attributes of multiple objects can be characterized simultaneously. This paper focuses on autoregression model of time series with matrix cross-section data and presents the methods of parameter estimation, model identification and white noise test. Finally, the daily yield series and daily volume change rate series of two bank stocks are analyzed by this model.

关键词

矩阵型截面数据时间序列 / 参数估计 / 似然比检验 / 白噪声检验

Key words

time series with matrix cross-section data / parameter estimation / likelihood ratio test / white noise test

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吴述金, 华楠. 矩阵型截面数据时间序列自回归模型. 数学学报, 2022, 65(6): 1093-1104 https://doi.org/10.12386/A20190057
Shu Jin WU, Nan HUA. Autoregression Model of Time Series with Matrix Cross-Section Data. Acta Mathematica Sinica, Chinese Series, 2022, 65(6): 1093-1104 https://doi.org/10.12386/A20190057

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基金

国家自然科学基金资助项目（11471120）
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