Fertility Prediction Models: Example of the Republic of Tuva
DOI:
https://doi.org/10.17059/ekon.reg.2023-3-14Abstract
Numerous Russian demographers and statisticians have considered the issues of predicting fertility. In recent years, the Federal State Statistics Service (Rosstat) has been publishing demographic forecasts, including data on the total fertility rate. However, despite extensive research, insufficient attention is paid to the analysis of the possibilities of using adaptive forecasting methods to assess the future dynamics of fertility. In this regard, the present study aims to build fertility prediction models for regions based on adaptive methods. The Republic of Tuva was chosen for testing as one of the unique constituent entities of the Russian Federation. During the implementation of the Concept of demographic policy, in particular maternity capital, the total fertility rate in Tuva did not fall below the replacement level fertility (2.14). Adaptive forecasting methods, such as ARIMA, Holt’s and Brown’s models, were utilised. In order to select the best prediction model, the study conducted a formal-logical analysis with a comparison of the main characteristics of the forecast accuracy and quality. The obtained results revealed promising development scenarios: moderately optimistic and regressive. The moderately optimistic scenario scientifically substantiated the feasibility of achieving fertility growth in the Republic of Tuva by 2025, focusing on the higher values of the average total fertility rate — 3.10 children per woman of reproductive age — that meets the goals of the demographic policy.
References
Abylkalikov, S. I. & Baimurzina, G. R. (2022). Demographic processes in the towns of Kyzyl and Elista in 2011-2020: a comparative study. Novye issledovaniya Tuvy [New Research of Tuva], 2, 34-52. DOI: https://www.doi.org/10.25178/nit.2022.2.3 (In Russ.)
Abylkalikov, S. I. (2021). Features of the Demographic Development of Tuva: Contribution of Migration to the Demographic Balance. Novye issledovaniya Tuvy [New Research of Tuva], 4, 131-142. DOI: https://www.doi.org/10.25178/nit.2021.4.10 (In Russ.)
Andreychik, M. F. & Kholshina, M. A. (2012). Demographic processes role in Tuva republic stabilized development. Vestnik KrasGAU [Bulletin of KrasGAU], 11, 10-16. Retrieved from: http://www.kgau.ru/vestnik/content/2012/11.12.pdf (Date of access: 20.02.2023) (In Russ.)
Arkhangelskiy, V. N. & Zayko, E. S. (2021). Lines of reproductive behavior. Problemy sotsialnoy gigieny, zdravookhraneniya i istorii meditsiny [Problems of social hygiene, public health and history of medicine], 29, 1374–1380. DOI: http://dx.doi.org/10.32687/0869-866X-2021-29-s2-1374-1380 (In Russ.)
Arkhangelskiy, V. N. (2019). Regional differentiation of fertility in Russia. In: Sotsialno-ekonomicheskie i demograficheskie aspekty realizatsii natsionalnykh proektov v regione: sbornik statey X Uralskogo demograficheskogo foruma. T. I [Socio-economic and demographic aspects of the implementation of national projects in the region: a collection of articles of the X Ural Demographic Forum, Volume I] (pp. 16-27). Ekaterinburg: Institute of Economics of the Ural Branch of RAS. Retrieved from: https://elar.urfu.ru/handle/10995/77572 (Date of access: 18.02.2023) (In Russ.)
Bakhitova, R. Kh., Lackman, I. A. & Shamsutdinova, N. K. (2016). Application of ARIMA-Models for Short-term Forecasting Fertility in the City of Ufa. Uroven zhizni naseleniya regionov Rossii [Living Standards of the Population in the Regions of Russia], 12(3), 214-219. DOI: 10.12737/22310 (In Russ.)
Balakina, G. F. (2022). Risks in human development in ethnic communities of the Republic of Tuva (1999-2021). Novye issledovaniya Tuvy [New Research of Tuva], 2, 20-33. DOI: https://www.doi.org/10.25178/nit.2022.2.2 (In Russ.)
Box, G. E. P. & Jenkins, G. M. (1970). Time series analysis: forecasting and control. San Francisco, CA: Holden-Day, 575.
Dai, J. & Chen, S. (2019). The application of ARIMA model in forecasting population data. Journal of Physics: Conference Series, 1324(1), 012100. DOI: https://doi.org/10.1088/1742-6596/1324/1/012100
Dubrova, T. A. (2019). Analysis of temporal data. In: V. S. Mkhitaryan (Ed.), Analiz dannykh [Data analysis] (pp. 397-459). Moscow: Yurait. (In Russ.)
Farida, Y., Farmita, M., Ulinnuha, N. & Yuliati, D. (2022). Forecasting Population of Madiun Regency Using ARIMA Method. CAUCHY: Jurnal Matematika Murni dan Aplikasi, 7(3), 420-431. DOI: https://doi.org/10.18860/ca.v7i3.16156
Frenkel, A. A. (1989). Prognozirovanie proizvoditelnosti truda: metody i modeli [Forecasting labor productivity: methods and models]. M.: Economics, 214. (In Russ.)
Garcia, F., Guijarro, F., Moya, I. & Oliver, J. (2012). Estimating returns and conditional volatility: A comparison between the ARMA-GARCH-M models and the backpropagation neural network. International Journal of Complex Systems in Science, 1(2), 21-26.
Gorbey, S., James, D. & Poot, J. (1999). Population forecasting with endogenous migration: An application to trans-Tasman migration. International Regional Science Review, 22(1), 69-101.
Keilman, N., Pham, D. Q. & Hetland, A. (2002). Why population forecasts should be probabilistic — illustrated by the case of Norway. Demographic Research, 6, 409-454.
Lamazhaa, Ch. K., Valiakhmetov, R. M. & Samba, A. D.-B. (2022). The problem field of studying human development in Tuva: a survey of literature and expert opinions (2020-2021). Novye issledovaniya Tuvy [New Research of Tuva], 2, 6-19. DOI: https://www.doi.org/10.25178/nit.2022.2.1 (In Russ.)
Lawrence, R. C. (1996). Forecasting U.S. mortality: A comparison of Box-Jenkins ARIMA and structural time series models. The Sociological Quarterly, 37, 127-144.
Mandyt, M. K. (2017). Republic of Tyva as a region of sustainable natural population growth. Uspekhi sovremennogo estestvoznaniya [Advances in current natural sciences], 7, 91-95. Retrieved from: https://natural-sciences.ru/ru/article/view?id=36484&ysclid=leklq67p83314466156 (Date of access: 20.02.2023) (In Russ.)
Maniatis, P. (2012). Forecasting The Exchange Rate Between Euro and USD: Probabilistic Approach Versus ARIMA And Exponential Smoothing Techniques. Journal of Applied Business Research (JABR), 28(2), 171-192. DOI: https://doi.org/10.19030/jabr.v28i2.6840
Mkhitaryan, S. V. & Danchenok, L. A. (2014). Sales forecasting using adaptive statistical methods. Fundamentalnye issledovaniya [Fundamental research], 9(4), 818-822. (In Russ.)
Ning, Y., Kazemi, H. & Tahmasebi, P., (2022). A comparative machine learning study for time series oil production forecasting: ARIMA, LSTM, and Prophet. Computers & Geosciences, 164, 105126. DOI: https://doi.org/10.1016/j.cageo.2022.105126.
Nyoni, T. (2019). The population question in Zimbabwe: reliable projections from the Box — Jenkins ARIMA approach. Munich Personal RePEc Archive No. 96791. Retrieved from: https://mpra.ub.uni-muenchen.de/96791/ (Date of access: 26.09.2021)
Pavlovskij, E. V. (2017). ARIMA models in the short-term forecasting of internal migration in Russia. Voprosy statistiki, 10, 53–63. (In Russ.)
Pilyugina, A. V. & Boyko, A. A. (2015). Using ARIMA models for forecasting of currency exchange rate. Prikaspiyskiy zhurnal: upravlenie i vysokie tekhnologii [Caspian Journal: Management and High Technologies], 4, 249-267. (In Russ.)
Rostovskaya, T. K. & Zolotareva, O. A. (2021). Professional Standard «Demograph» as a Factor of Formation of a New Human Resources Model. Sotsiologicheskaya nauka i sotsialnaya praktika [Sociological Science and Social Practice], 9(2), 82-95. DOI: https://doi.org/10.19181/snsp.2021.9.2.8106 (In Russ.)
Rostovskaya, T. K. & Zolotareva, O. A. (2022). Professional Standard “Demographer” as a Key Mechanism for Managing the Development of Human Resources in the Field of People Saving. Sotsiologicheskaya nauka i sotsialnaya praktika [Sociological Science and Social Practice], 10(1), 128-142. DOI: https://doi.org/10.19181/snsp.2022.10.1.8865 (In Russ.)
Rostovskaya, T. K., Zolotareva, O. A. & Davletshina, L. A. (2023). Features of birth rate in the Republic of Tuva (1991–2021). Novye issledovaniya Tuvy [New Research of Tuva], 2, 34-49. DOI: https:// doi.org/10.25178/nit.2023.2.3 (In Russ.)
Ruppert, D. & Matteson, D. S. (2015). Statistics and Data Analysis for Financial Engineering. Springer, 719. DOI: https://doi.org/10.1007/978-1-4939-2614-5
Sadovnikova, N. A. & Zolotareva, O. A. (2020). COVID-19 in Russia: Realities Justified by Statistical Analytics and Forecasting. Ekonomicheskie strategii [Economic strategies], 4(170), 42-53. DOI: 10.33917/es-4.170.2020.42-53 (In Russ.)
Sevek, V. K., Sotnikov, A. I., Manchyk-Sat, Ch. S., Chul’dum, A. E. & Sereezhikpei, A. A. (2017). Monitoring and forecast of the basic indicators of living standards of the population of the Republic of Tyva. Ekonomika: vchera, segodnya, zavtra [Economics: Yesterday, Today and Tomorrow], 7(7A), 100-119. Retrieved from: http://www.publishing-vak.ru/file/archive-economy-2017-7/9-sevek.pdf (Date of access: 20.02.2023) (In Russ.)
Sun, J. (2021). Forecasting COVID-19 pandemic in Alberta, Canada using modified ARIMA models. Computer methods and programs in biomedicine update, 1, 100029. DOI: https://doi.org/10.1016/j.cmpbup.2021.100029
Swaraj, A., Verma, K., Kaur, A., Singh, G., Kumar, A. & Melo de Sales, L. (2021). Implementation of stacking based ARIMA model for prediction of Covid-19 cases in India. Journal of biomedical informatics, 121, 103887. DOI: https://doi.org/10.1016/j.jbi.2021.103887
Valiakhmetov, R. M., Zaripov, A. Ya. & Turakayev, M. S. (2022). Issues of human potential development in the republics of the Russian Federation (according to the results of a survey of experts in Dagestan and Tuva). Novye issledovaniya Tuvy [New Research of Tuva], 3, 170-185. DOI: https://www.doi.org/10.25178/nit.2022.3.12 (In Russ.)
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Ростовская Тамара Керимовна , Золотарева Ольга Анатольевна
This work is licensed under a Creative Commons Attribution 4.0 International License.
