Advances in Economics, Management and Political Sciences
- The Open Access Proceedings Series for Conferences
Proceedings of the 2nd International Conference on Financial Technology and Business Analysis
Series Vol. 59 , 05 January 2024
Open
Access | Article
Modeling and Prediction of Birth Rate in China
* Author to whom correspondence should be addressed.
Abstract
Concerns about a persistent reduction are growing against the backdrop of large changes in China’s birth rate during the last few decades. This paper explores this tendency via the lens of time series analysis, using birth rate records from 1964 through 2021 (particularly ignoring 1960-1963 due to Great Leap Forward distortions). The ARIMA and ETS models were extensively studied in our hunt for the best accurate forecasting device. The ARIMA (0,0,1) model was considered to be preferred based on comparison measures. The primary goal of this model was to predict the trend of China’s fertility rates over the following five years. The ARIMA an1``d ETS models were rigorously applied to a selected training set after initial adjustments to ensure data stationarity, followed by an evaluation of their accuracy. Our findings, which are backed by the ARIMA model, imply a disturbing trend: a 0.117 percent annual fall in China’s birth rate from 2022 to 2026. This suggests that a national fertility crisis is on the horizon. As a first step, we advise looking at the various socioeconomic reasons that may be driving this trend, as well as evaluating policy actions that could serve as potential cures.
Keywords
Birth rate, Time series analysis, ARIMA model, ETS model, Forecasting
References
1. Kohler, H.-P. (2001). Social Interactions and Fluctuations in Birth Rates. Oxford University Press EBooks, 145–182.
2. CAO, S., & WANG, X. (2009). DEALING WITH CHINA’S FUTURE POPULATION DECLINE: A PROPOSAL FOR REPLACING LOW BIRTH RATES WITH SUSTAINABLE RATES. Journal of Biosocial Science, 41(5), 693-696.
3. Hasan, N. I. A., Aziz, A. A., Ganggayah, M. D., Jamal, N. F., & Ghafar, N. M. A. (2022). Projection of Infant Mortality Rate in Malaysia using R. Jurnal Sains Kesihatan Malaysia, 20(2), 23-36.
4. Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: principles and practice. OTexts.
5. Wei, Y., Wang, Z., Wang, H., Li, Y., & Jiang, Z. (2019). Predicting population age structures of China, India, and Vietnam by 2030 based on compositional data. PLoS One, 14(4), e0212772.
6. Karunanidhi, D., & Sasikala, S. (2023). Robustness of Predictive Performance of Arima Models Using Birth Rate of Tamilnadu.
7. Yang, S., Jiang, Q., & Jesús S.J. (2022). China’s fertility change: an analysis with multiple measures.
8. Jain, G., & Mallick, B. (2017). A study of time series models ARIMA and ETS. Available at SSRN 2898968.
9. Liu, Q., Charleston, M. A., Richards, S. A., & Holland, B. R. (2022). Performance of AIC and BIC in Selecting Partition Models and Mixture Models. Systematic Biology.
10. Takayama, N., & Werding, M. (Eds.). (2010). Fertility and Public Policy. The MIT Press.
Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. Authors who publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open Access Instruction).
- Volume Title
- Proceedings of the 2nd International Conference on Financial Technology and Business Analysis
- ISBN (Print)
- 978-1-83558-209-1
- ISBN (Online)
- 978-1-83558-210-7
- Published Date
- 05 January 2024
- Series
- Advances in Economics, Management and Political Sciences
- ISSN (Print)
- 2754-1169
- ISSN (Online)
- 2754-1177
- DOI
- 10.54254/2754-1169/59/20231010
- Copyright
- 05 January 2024
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited