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№3 (41) 2020

Demography and social economy, 2020, 3(41):76-90
doi: https://doi.org/10.15407/dse2020.03.076
UDC 314.303.(477)
JEL Сlassifi cation: R10

PhD (Economics), Leading scientist
Ptoukha Institute for Demography and Social
Studies of the National Academy of Sciences of Ukraine
01032, Ukraine, Kyiv, Вlvd Taras Shevchenko, 60
E-mail: pavlo-shevchuk@ukr.net
ORCID 0000-0003-1158-4438

PhD (Engineering), Associate professor,
Taras Schevchenko National University of Kyiv,
04116, Ukraine, Kyiv, Bohdana Havrylyshyna Str., 24
E-mail: alexee@ukr.net
ORCID 0000-0003-1390-5311

Language: English
Abstract: Uncertainty is an intrinsic characteristic of demographic processes. Th is applies even more to the future. Accurate deterministic forecasts are fundamentally impossible. Th is determines the necessity to quantify the future uncertainty. Th e purpose of this research is to develop probabilistic demographic forecasts for the metropolises of Ukraine and analyze the outcome results. For the fi rst time, probabilistic demographic forecasts have been developed for individual cities of Ukraine. Th e study was carried out using the functional data approach which incorporates wide set of demographical methods and models implemented in several packages of R programming language. Chosen methodology is based entirely on statistics and does not require introducing any additional arbitrary hypotheses. At three cases (namely for fertility in Kyiv, Lviv and Kharkiv) the default method (ARIMA) showed implausible results which could be induced by unreliable current data. In these cases were used random walk model. For Odesa the both models give similar results. It is possible that in this city the underestimation of the departed population is compensated by the underestimation of the arrived, which leads to the relevance of the current fertility rates (namely their denominators) and, consequently, the consistency of the forecast results regardless of the method. Mortality forecasts are consistent with the dynamics of mortality rates being observed and the quality of current data. Th e model captured upward life expectancy trends for Dnipro and Odesa and stagnation for other cities. Th is is also could be caused by denominator inconsistencyfor the latter ones. Computation showed that the population size of Dnipro, Lviv and Kharkiv in 2040 is most likely to be below the population number reached in early 2019. Some chances for population growth remain in Odesa and Kyiv is likely to have a larger population. Th e age distribution of the population in all cities in future looks similar. The number of people over 40 years of age has least uncertainty. At the age of 20 to 40 years, the uncertainty is much higher. Th is is a consequence of the uncertainty of youth migration during the forecast horizon of 2019–2039, because all these cities are powerful educational centres and attract students. In 2040 those who were students in 2020 will reach the age of 40 and can stay in the big city or leave. Uncertainty of the number of persons under 20 is formed from two sources: uncertainty of fertility forecasts and uncertainty of the number of reproductive cohort, i.e. those 20-40-year-olds. It is needed to review these forecasts aft er receiving the results of the closest census.
Key words: probabilistic forecast, functional time series, uncertainty, big city.
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