TFR Predictions Based on Brownian Motion Theory

In stochastic population forecasts, the predictive distribution of the TFR is of centralconcern. Common time series models can be used to predict the TFR and itsmoments on the short run (up to 10 or 20 years), but on the long run (40-50 years)they result in excessively wide prediction intervals. The aim of this study is toanalyse and apply a time series model for the TFR, which restricts the predictedvalues to a certain pre-specified interval.I will model the time series of log TFR-values as a Brownian motion with absorbingupper barrier. I will give and analyse expressions for the predictive distribution of the log of the TFR assuming itfollows a Brownian motion with absorbing ceiling; expressions for the first and second moments of the predictive distribution ofthe log of the TFR.When the log of the TFR follows a random walk with absorbing ceiling, I find thatthe second moment of the predictive distribution for the long-run TFR in Norwayis insensitive for ceiling levels beyond a threshold of approximately 3.4 childrenper woman. This conclusion holds for a fairly broad range of innovation variances.If the log of the TFR follows a random walk, sample paths that exceed approximately3.4 children per woman may be rejected when simulating future fertility in Westerncountries. This will not have any major effect on the width of the long-termpredictive distribution

An editorial on plagiarism

In January this year, we were confronted with a case of plagiarism. One paper that had been submitted last year by a certain person turned out to be written by three other persons. It was presented by the three true authors at a conference in 2010, where they distributed copies of their paper. One of the reviewers of the paper informed us about that fact. We asked the three authors for a copy, which turned out to be identical with the submission, except for a few minor details. When confronted with these facts, the person who had submitted the paper was unable to give us a satisfactory explanation. This is a case of serious scientific misconduct. The editors and the publisher of Demographic Research cannot and will not accept any form of plagiarism. Nor will we accept any other form of misconduct in science, including fabrication, falsification, or other practices that seriously deviate from those that are commonly accepted within the scientific community for proposing, conducting, or reporting research. With Long et al. ("Responding to possible plagiarism", Science 6 March 2009), we are of the opinion that the responsibility for research integrity ultimately lies in the hands of the scientific community: educators, students, authors, and those who provide peer reviews. Journal editors must take appropriate action and verify the originality of suspected manuscripts. The Office of Research Integrity provides useful guidelines (http://ori.dhhs.gov/). We have decided that any future submission to Demographic Research that lists the plagiarist as an author or co-author will be rejected automatically.

Why population forecasts should be probabilistic - illustrated by the case of Norway

Deterministic population forecasts do not give an appropriate indication of forecast uncertainty. Forecasts should be probabilistic, rather than deterministic, so that their expected accuracy can be assessed. We review three main methods to compute probabilistic forecasts, namely time series extrapolation, analysis of historical forecast errors, and expert judgement. We illustrate, by the case of Norway up to 2050, how elements of these three methods can be combined when computing prediction intervals for a population’s future size and age-sex composition. We show the relative importance for prediction intervals of various sources of variance, and compare our results with those of the official population forecast computed by Statistics Norway.cohort component method, forecast errors, forecasting, simulation, stochastic population forecast, time series, uncertainty

Measures for Human Reproduction Should Be Linked to Both Men and Women

We introduce the two-sex net reproduction rate (2SNRR) and the two-sex total fertility rate (2STFR)—two demographic indicators that reflect the number of children born, given age specific fertility and mortality of the adults. The main quality of these indicators is that they measure the childbearing behaviour of both women and men. The indicators have intuitive value, since they tell us to what extent adults are replaced by children. While the traditional net reproduction rate (NRR) describes general replacement trends among women only, the 2SNRR is an indicator of a population’s growth potential, irrespective of sex. We demonstrate the use of the indicators with data from Bejsce parish in Poland for the period 1800–1967 and with data from UN projections for China for future years. We discuss the consequences for our understanding of fertility trends when sex ratios deviate from normal levels

Augmenting migration statistics with expert knowledge

International migration statistics vary considerably from one country to another in terms of measurement, quality and coverage. Furthermore, immigration tend to be captured more accurately than emigration. In this paper, we first describe the need to augment reported flows of international migration with knowledge gained from experts on the measurement of migration statistics, obtained from a multi-stage Delphi survey. Second, we present our methodology for translating this information into prior distributions for input into the Integrated Modelling of European Migration (IMEM) model, which is designed to estimate migration flows amongst countries in the European Union (EU) and European Free Trade Association (EFTA), by using recent data collected by Eurostat and other national and international institutions. The IMEM model is capable of providing a synthetic data base with measures of uncertainty for international migration flows and other model parameters.

Utilising expert opinion to improve the measurement of international migration in Europe

In this article, we first discuss the need to augment reported flows of international migration in Europe with additional knowledge gained from experts on measurement, quality and coverage. Second, we present our method for eliciting this information. Third, we describe how this information is converted into prior distributions for subsequent use in a Bayesian model for estimating migration flows amongst countries in the European Union (EU) and European Free Trade Association (EFTA). The article concludes with an assessment of the importance of expert information and a discussion of lessons learned from the elicitation process.<br/

Assumptions for long-term stochastic population forecasts in 18 European countries: Hypothèses de projections stochastiquesàlong terme des populations de 18 pays européens

The aim of the ‘Uncertain Population of Europe’(UPE) project was to compute long-term stochastic (probabilistic) population forecasts for 18 European countries. We developed a general methodology for constructing predictive distributions for fertility, mortality and migration. The assumptions underlying stochastic population forecasts can be assessed by analysing errors in past forecasts or model-based estimates of forecast errors, or by expert judgement. All three approaches have been used in the project. This article summarizes and discusses the results of the three approaches. It demonstrates how the—sometimes conflicting—results can be synthesized into a consistent set of assumptions about the expected levels and the uncertainty of total fertility rate, life expectancy at birth of men and women, and net migration for 18 European countries

Probabilistic household forecasts for five countries in Europe

We show how techniques of data dimension reduction can be used to predict patterns of household dynamics in a multi-country context. Probabilistic household forecasts are presented for Denmark, Finland, Germany, the Netherlands, and Norway, spanning the period 2011-2041. Starting point is the population of each country broken down by age, sex, and household position as reported in the census round of 2011. Future trends in fertility, mortality and international migration are taken from official population forecasts. For changes in household structure we rely on time series of household data. Long series of household data, in which the population is broken down by household position, age, and sex, are available for Denmark (1981-2007) and Finland (1988-2009) from the population registers in these countries. For the Netherlands the series are rather short (1995-2011). Annual shares of the population by household position, age, and sex for the three time series countries are modeled using an approach that builds on Brass’ relational model originally developed to model the age pattern of mortality. We find that the household shares can be modelled as Random Walks with Drifts (RWD), independent of country. The Brass approach preserves the age patterns of the household shares. Future household shares are found by extrapolating the RWD processes. This results in household share forecasts, as well as standard errors of the forecasts. Correlations across ages and between men and women are estimated from model residuals. No time series data are available for Germany or Norway. For Germany, we use household transition rates borrowed from Denmark and Finland, but adjusted to cohabitation and marriage levels from the German Generation and Gender Survey. For Norway, we have household transition rates for the year 2010 from the population register. Future household patterns for these two countries are computed by using the multistate household model LIPRO, in which the household transition rates are applied to the household pattern from the census. Uncertainty parameters are borrowed from the time series analyses for Denmark, Finland and the Netherlands. The results show a continuation of current trends towards more and smaller households, often driven by increasing numbers of persons who live alone. The number of households increases faster than population size, which leads to falling average household size. A very consistent finding is that larger households are easier to predict than smaller households, at least when uncertainty is considered in a relative sense