A Principal Component Simulation of Age-Specific Fertility – Impacts of Family and Social Policy on Reproductive Behavior in Germany

Vanella, Patrizio; Deschermeier, Philipp

doi:10.1353/prv.2019.0002

Cited by 16 publications

(55 citation statements)

References 42 publications

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“…We derive age-specific fertility rates (ASFRs) by dividing age-specific births by the corresponding mean age-specific female population for the respective year. As proposed by Vanella and Deschermeier (2018b), we run a PCA on the ASFRs for mothers aged 13-54 years for the base period 1968-2016. This time horizon was proposed in that paper because it shows fertility developments after the second wave of the feminist movement.…”

Section: Methods and Datamentioning

confidence: 99%

A Probabilistic Cohort-Component Model for Population Forecasting – The Case of Germany

Vanella

Deschermeier

2020

Population Ageing

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The future development of population size and structure is of importance since planning in many areas of politics and business is conducted based on expectations about the future makeup of the population. Countries with both decreasing mortality and low fertility rates, which is the case for most countries in Europe, urgently need adequate population forecasts to identify future problems regarding social security systems as one determinant of overall macroeconomic development. This contribution proposes a stochastic cohort-component model that uses simulation techniques based on stochastic models for fertility, migration and mortality to forecast the population by age and sex. We specifically focus on quantifying the uncertainty of future development as previous studies have tended to underestimate future risk. The results provide detailed insight into the future population structure, disaggregated into both sexes and 116 age groups. Moreover, the uncertainty in the forecast is quantified as prediction intervals for each subgroup. The underlying models for forecasting the demographic components have been developed in earlier studies and rely on principal component time series models. Since the proposed model is fully probabilistic, it offers a wide range of information, not only identifying the most probable course of the population but also a vast number of possible scenarios for future development of the population and quantifying their respective likelihoods. The model is applied to forecast the population of Germany until 2040. 2 The results indicate a larger future population for Germany compared to the population predicted in studies conducted before 2015. The driving factors are lower mortality, higher fertility and higher net migration as derived by us statistically in contrast to widely used qualitative assumptions. The present study shows that the increase in population is mainly due to a larger proportion of older individuals.

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Section: Methods and Datamentioning

confidence: 99%

A Probabilistic Cohort-Component Model for Population Forecasting – The Case of Germany

Vanella

Deschermeier

2020

Population Ageing

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“…Selected simulations can also be used to illustrate individual, stylized developments and their consequences, even if they are not expected to occur in this form in the future. For example, a simulation could be used to estimate how the population would develop if the total fertility rate reached a replacement-level of 2.1 children per woman [4].…”

Section: Population Projections-concepts and Purposementioning

confidence: 99%

“…The demographic components are highly dependent on the demographic structure of the underlying population. For instance, the birth numbers are relatively high, if a large share of the females is in the age group 25-35 years [4]. A very old population is associated with many deaths [12].…”

Section: Conceptual Approachesmentioning

confidence: 99%

“…Migration is relatively frequent among small children and young adults due to labor and study reasons [18,19]. Therefore, population projections are commonly done via demographic rates instead of crude numbers [3,4,7,17,[20][21][22][23][24]. This is difficult in the case of immigration however, see, e.g., [7] on that.…”

Section: Conceptual Approachesmentioning

confidence: 99%

“…The only disputed issue is whether historical developments can be seen as representative of future developments. Vanella and Deschermeier [4] show, however, using the example of the total fertility rate, that a stochastic frequentist approach based on good data can achieve better forecasts than qualitative approaches do. This, however, does not always apply and depends very much on the variable and the available data.…”

Section: Conceptual Approachesmentioning

confidence: 99%

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An Overview of Population Projections—Methodological Concepts, International Data Availability, and Use Cases

2020

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Population projections serve various actors at subnational, national, and international levels as a quantitative basis for political and economic decision-making. Usually, the users are no experts in statistics or forecasting and therefore lack the methodological and demographic background to completely understand methods and limitations behind the projections they use to inform further analysis. Our contribution primarily targets that readership. Therefore, we give a brief overview of different approaches to population projection and discuss their respective advantages and disadvantages, alongside practical problems in population data and forecasting. Fundamental differences between deterministic and stochastic approaches are discussed, with special emphasis on the advantages of stochastic approaches. Next to selected projection data available to the public, we show central areas of application of population projections, with an emphasis on Germany.

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Assessing excess mortality in times of pandemics based on principal component analysis of weekly mortality data—the case of COVID-19

Vanella

Basellini

Lange

2021

Genus

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The COVID-19 outbreak has called for renewed attention to the need for sound statistical analyses to monitor mortality patterns and trends over time. Excess mortality has been suggested as the most appropriate indicator to measure the overall burden of the pandemic in terms of mortality. As such, excess mortality has received considerable interest since the outbreak of COVID-19 began.Previous approaches to estimate excess mortality are somewhat limited, as they do not include sufficiently long-term trends, correlations among different demographic and geographic groups, or autocorrelations in the mortality time series. This might lead to biased estimates of excess mortality, as random mortality fluctuations may be misinterpreted as excess mortality.We propose a novel approach that overcomes the named limitations and draws a more realistic picture of excess mortality. Our approach is based on an established forecasting model that is used in demography, namely, the Lee-Carter model. We illustrate our approach by using the weekly age- and sex-specific mortality data for 19 countries and the current COVID-19 pandemic as a case study. Our findings show evidence of considerable excess mortality during 2020 in Europe, which affects different countries, age, and sex groups heterogeneously. Our proposed model can be applied to future pandemics as well as to monitor excess mortality from specific causes of death.

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A Principal Component Simulation of Age-Specific Fertility – Impacts of Family and Social Policy on Reproductive Behavior in Germany

Cited by 16 publications

References 42 publications

A Probabilistic Cohort-Component Model for Population Forecasting – The Case of Germany

A Probabilistic Cohort-Component Model for Population Forecasting – The Case of Germany

An Overview of Population Projections—Methodological Concepts, International Data Availability, and Use Cases

Assessing excess mortality in times of pandemics based on principal component analysis of weekly mortality data—the case of COVID-19

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