Autoregression Model of Time Series with Matrix Cross-Section Data

Shu Jin Wu; Nan Hua

doi:10.12386/A20190057

Autoregression Model of Time Series with Matrix Cross-Section Data

Shu Jin Wu
, Nan Hua

School of Statistics

East China Normal University

Research output: Contribution to journal › Article › peer-review

3 Scopus citations

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.

Original language	English
Pages (from-to)	1093-1104
Number of pages	12
Journal	Acta Mathematica Sinica, Chinese Series
Volume	65
Issue number	6
DOIs	https://doi.org/10.12386/A20190057
State	Published - 15 Nov 2022

Keywords

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

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10.12386/A20190057

Cite this

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title = "Autoregression Model of Time Series with Matrix Cross-Section Data",

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.",

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AU - Hua, Nan

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AB - 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.

KW - likelihood ratio test

KW - parameter estimation

KW - time series with matrix cross-section data

KW - white noise test

UR - https://www.scopus.com/pages/publications/85158898511

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DO - 10.12386/A20190057

M3 - 文章

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JO - Acta Mathematica Sinica, Chinese Series

JF - Acta Mathematica Sinica, Chinese Series

IS - 6

ER -

Autoregression Model of Time Series with Matrix Cross-Section Data

Abstract

Keywords

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