Fertility Prediction Models: Example of the Republic of Tuva; [Моделирование прогноза рождаемости на примере Республики тыва1]

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. © 2023 Institute of Economics, Ural Branch of the Russian Academy of Sciences. All rights reserved.

Авторы
Rostovskaya T.K. , Zolotareva O.A.
Издательство
Institute of Economics, Ural Branch of the Russian Academy of Sciences
Номер выпуска
3
Язык
Русский
Страницы
801-812
Статус
Опубликовано
Том
19
Год
2023
Организации
  • 1 Institute for Demographic Research FCTAS RAS, Moscow, Russian Federation
  • 2 RUDN University, Moscow, Russian Federation
  • 3 Lomonosov Moscow State University, Moscow, Russian Federation
Ключевые слова
demographic development scenarios; fertility; fertility prediction; forecasting methods; Republic of Tuva; total fertility rate
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