Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/155634
Authors: 
Vanella, Patrizio
Year of Publication: 
2016
Series/Report no.: 
Hannover Economic Papers (HEP) 579
Abstract: 
Demographic change is one of the greatest challenges faced by Germany as well as a large part of Europe today. One of the main drivers of this change is the low fertility level, often referred to as the Total Fertility Rate (TFR), since the early 1970s. Therefore, on the one hand, while the total population is expected to decline, on the other hand, the relative share of the elderly in the total population is expected to increase. This poses a great challenge for the society in a wide range of aspects, most notably in the statutory pension fund. Therefore, it is important to gain an understanding of the future demographic development, in our case, the course of the TFR. Official forecasts often assume that the TFR will remain at a low level of 1.4 in the long run, which was already proven wrong in the publication of the 2014 data, which shows a TFR of 1.47. However, separate analysis of age-specific fertility lead to expected increases of the future TFR. This study presents a stochastic projection of the TFR based on econometric-statistical modeling of age-specific fertility rates over principal components. Simulation techniques not only generate the expected future TFR until the year 2040, but also provide point-wise prediction intervals which cover the future TFR with a probability of 95\% annually based on the current data set. The age-specific structure of the modeling procedure gives a detailed insight of the future development of the reproductive behavior for women in Germany, and therefore, is very informative with regard to possible political intervention with the scope of increasing the TFR. Moreover, the flexible structure of the model allows more sophisticated estimations of future outcome of certain political measures.
Subjects: 
Fertility Projection
Applied Principal Component Analysis
Applied Time Series Analysis
JEL: 
C53
J11
J13
Document Type: 
Working Paper

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