Time-series prediction algorithm based on VAR model to analyze the impact of trade openness on carbon emissions

DOI

10.2991/978-94-6463-262-0_80How to use a DOI?

Keywords

Time-series prediction algorithm; VAR model; Eviews analysis

Abstract

Based on time-series statistical data from 1990 to 2020, this study empirically studied the impact of trade openness on carbon emissions changes through two representative explanatory variables, namely foreign trade dependence and foreign investment dependence, using Eviews software through cointegration tests, impulse response functions, and variance decomposition methods based on VAR model from time-series prediction algorithm. The impulse response function and variance decomposition methods were used to analyze the dynamic shock effects and the degree of influence of foreign trade dependence and foreign investment dependence on carbon emissions respectively. Based on the VAR model results, foreign trade dependence and foreign investment dependence can affect the carbon emissions.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Ye Huang
AU  - Yueting Jiang
PY  - 2023
DA  - 2023/10/09
TI  - Time-series prediction algorithm based on VAR model to analyze the impact of trade openness on carbon emissions
BT  - Proceedings of the 3rd International Conference on Management Science and Software Engineering (ICMSSE 2023)
PB  - Atlantis Press
SP  - 784
EP  - 792
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-262-0_80
DO  - 10.2991/978-94-6463-262-0_80
ID  - Huang2023
ER  -