| Abstract [eng] |
The object of research is statistical data from the Lithuanian Department of Statistics on the aging index of the Lithuanian population and the demographic factors that negatively affect it: the population rate, the number of birth, death, migration, marriage, and divorce rate during 2002-2019. The aim of the work is to statistically investigate the aging of the population in Lithuania and the changes in related factors. In order to do this, several objectives have been set: to find the optimal grouping of counties into four groups, so that the population would be similar. To recalculate the data according to the newly obtained groups. To determine how the aging index shifts in all regions during the investigated period of 2002-2019. Calculation of negative factors by regions during 2002-2019. To investigate the dynamics of changes in the data by employing the statistical hypothesis of a polynomial distribution and to display the data on maps of Lithuania. By employing regression analysis, to identify the correlation of the aging index with other indicators, find appropriate linear regression models. To determine which data series can be described by time series models, to find the AR(1) equations after eliminating the trend. By employing four demographic forecasting models, to identify the most appropriate forecasting model, to make a forecast regarding the groups aging index for 2025. The obtained results of the MA thesis show that the aging of the Lithuanian population can best be investigated by grouping Lithuanian counties according to their population into the northern, western, eastern, and south-central regions. It was found that the aging index in 2002-2008 increased similarly in all regions. Critical aging in all regions started between 2006 and 2009. During the financial crisis, aging indices increased most in the northern region. Data on negative factors by region for the period of 2002-2019 is found. By employing the hypothesis of a polynomial distribution, it was found that there were significant changes every five years in the population, migration soldo, and aging index data during 2010-2014, the dynamics of the factors being displayed in the maps. The highest correlation of the aging index with the population was identified, an empirical correlation index is -0.98. It was identified which data series are suitable for time series models and AR(1) models allowing to forecast the data were found. The most appropriate models for the forecast of the aging index are the geometric growth model, based on the model it is forecasted that aging indices in 2025 will be 123 in the eastern region and 200 in the northern region. The arithmetic growth model fits in the western region – 163 and 178 in the south-central region. If nothing changes, the aging index will continue to increase rapidly in all regions. |
