An Empirical Study on Influencing Factors of China's Tax Revenue Based on Principal Component Regression Model

DOI

10.2991/978-94-6463-198-2_63How to use a DOI?

Keywords

Tax income; Influencing factor; Principal component analysis; BP neural network method

Abstract

This paper analyzes the influencing factors of tax revenue from an empirical point of view, adopts the principal component analysis method to reduce the dimension and simplifies the regression model, and on this basis introduces the BP neural network method to predict the tax revenue. The empirical research conclusion shows that the national fiscal expenditure has the greatest impact on tax revenue, with a corresponding elasticity coefficient of 43.89%, followed by the total fixed asset investment and the added value of the tertiary industry, and the second industry’s added value and social consumer goods retail sales have a relatively small impact. The results can provide a useful reference for the formulation of China's tax policy.

Copyright

© 2023 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  - Lijuan Zeng
AU  - Linxin Liu
PY  - 2023
DA  - 2023/08/10
TI  - An Empirical Study on Influencing Factors of China's Tax Revenue Based on Principal Component Regression Model
BT  - Proceedings of the 2nd International Academic Conference on Blockchain, Information Technology and Smart Finance (ICBIS 2023)
PB  - Atlantis Press
SP  - 615
EP  - 627
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-198-2_63
DO  - 10.2991/978-94-6463-198-2_63
ID  - Zeng2023
ER  -