<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In this paper, a penalty function is added in the construction of the VAR model to control the accuracy of the model prediction, and considering the nature of the correlation between the variables, a weight fusion with Laplace penalty is introduced to generate a sparse solution to complete the construction of the vector autoregressive model. The basic types of vector autoregressive models under panel data are divided, the number of parameters to be estimated is determined, the parameters of the vector autoregressive model are estimated by the generalized method of moments estimation, and the parameter fitting effect of the model is improved by the great likelihood parameter estimation method, and the vector autoregressive model for panel data is constructed. Based on the VAR model, we propose a design scheme for the teaching of Civics in college sports courses and analyze the development of sports + Civics and the teaching effect of the courses through simulation and empirical analysis. The results of the study showed that OSI and OPCI rejected the original hypothesis at the significance level of 5% after second-order differencing, at which time both p-values were less than 0.1, and the series was smooth. The elasticity coefficient of the starting value of the sports response path is 0.0342, and the error interval is [0.0564, 0.06], indicating that the future development of the scale of “Sports + Civics” is stable.</p>
</abstract>

In this paper, a penalty function is added in the construction of the VAR model to control the accuracy of the model prediction, and considering the nature of the correlation between the variables, a weight fusion with Laplace penalty is introduced to generate a sparse solution to complete the construction of the vector autoregressive model. The basic types of vector autoregressive models under panel data are divided, the number of parameters to be estimated is determined, the parameters of the vector autoregressive model are estimated by the generalized method of moments estimation, and the parameter fitting effect of the model is improved by the great likelihood parameter estimation method, and the vector autoregressive model for panel data is constructed. Based on the VAR model, we propose a design scheme for the teaching of Civics in college sports courses and analyze the development of sports + Civics and the teaching effect of the courses through simulation and empirical analysis. The results of the study showed that OSI and OPCI rejected the original hypothesis at the significance level of 5% after second-order differencing, at which time both p-values were less than 0.1, and the series was smooth. The elasticity coefficient of the starting value of the sports response path is 0.0342, and the error interval is [0.0564, 0.06], indicating that the future development of the scale of “Sports + Civics” is stable.

Research on the Design and Practice of Civics Teaching in College Physical Education Courses Based on VAR Modeling

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

In this paper, a penalty function is added in the construction of the VAR model to control the accuracy of the model prediction, and considering the nature of...

Research on the Design and Practice of Civics Teaching in College Physical Education Courses Based on VAR Modeling

Published Online: Dec 20, 2023

Page range: -

Received: Feb 04, 2023

Accepted: Jun 30, 2023

DOI: https://doi.org/10.2478/amns.2023.2.01549

Keywords
VAR model, Weight fusion, Laplace penalty, Parameter estimation, Great likelihood method

© 2023 Jun Fan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research on the Design and Practice of Civics Teaching in College Physical Education Courses Based on VAR Modeling

Published Online: Dec 20, 2023

Page range: -

Received: Feb 04, 2023

Accepted: Jun 30, 2023

DOI: https://doi.org/10.2478/amns.2023.2.01549

KeywordsVAR model, Weight fusion, Laplace penalty, Parameter estimation, Great likelihood method

© 2023 Jun Fan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Keywords
VAR model, Weight fusion, Laplace penalty, Parameter estimation, Great likelihood method