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Keilman, Nico
(2022).
Recent demographic trends in Nordic countries, with a focus on fertility in Finland and Norway.
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Keilman, Nico
(2021).
New population forecasts predict too few births in sub-Saharan countries.
The Lancet.
ISSN 0140-6736.
398.
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Keilman, Nico
(2020).
A probabilistic forecast for the population of Norway,
Norway’s 2020 population projections: National level results, methods and assumptions.
Statistics Norway.
ISSN 978-82-587-1149-7.
s. 177–182.
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Feeney, Griffith; Keilman, Nico; Schmertmann, Carl & Bijak, Jakub
(2019).
Editorial: The past, present, and future of demographic research.
Demographic Research.
ISSN 1435-9871.
41,
s. 1197–1204.
doi:
10.4054/DEMRES.2019.41.41.
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Bengtsson, Tommy & Keilman, Nico
(2019).
Preface.
Demographic Research Monographs.
ISSN 1613-5520.
s. vii–viii.
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Keilman, Nico
(2019).
Probabilistic household and living arrangement forecasts.
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Keilman, Nico
(2019).
Probabilistic household and living arrangement forecasts.
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Bengtsson, Tommy; Keilman, Nico; Alho, Juha; Christensen, Kaare; Palmer, Edward & Vaupel, James W.
(2019).
Introduction.
I Bengtsson, Tommy & Keilman, Nico (Red.),
Old and New Perspectives on Mortality Forecasting.
Springer Nature.
ISSN 978-3-030-05074-0.
s. 1–19.
Vis sammendrag
More than 10 years have gone since the Swedish Social Insurance Agency published a series of booklets entitled Perspectives on Mortality Forecasting. Five volumes appeared in print, on different topics that all are relevant for anyone faced with the task of computing a forecast of mortality in future years. Each volume contained the papers presented in a series of workshops organized by the Stockholm Committee on Mortality Forecasting during the years 2002–2007. The current volume contains reprints of the contributions to the five booklets. Each part of this book corresponds to one original booklet.
The field of mortality forecasting is in continuous change. The purpose of the current volume is to track this development by showing the reader what the main issues were some 10–15 years ago, together with an update. Therefore, the book starts with an introductory chapter, which summarizes recent new insights on the following topics:
– The need for accurate mortality forecasting today for systems for pension, health care, and elderly care
– Determinants and dynamics of life expectancy
– Causes of death and lifestyle factors, such as smoking and obesity
– Cohort and period perspectives
– Compression of mortality
– Joint forecasting of mortality in similar populations
– From scenarios to stochastic modelling
– The way conditions in early life affect mortality in later life
– The increasing gap in life expectancy with respect to position in the income distribution
Some of the material in the introductory chapter relates to only one of the five sections, while other items in the list above cut across themes. The last item brings up a new topic of mortality forecasting, which was not dealt with in any of the workshops.
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Keilman, Nico
(2018).
Training Course on Demographic Analysis and Population Projections, Pristina, Kosovo, 4-7 December 2018
.
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Keilman, Nico
(2018).
Forventet levealder og komprimering av dødelighet: perioder og kohorter.
Vis sammendrag
Forventet levealder beregnet for et bestemt kalenderår kan gi et misvisende bilde av den virkelige levealderen. Jeg viser hvordan periode-levealderen endrer seg når aldersmønsteret for dødelighet i kohorter endrer seg. Jeg analyserer også komprimering av dødelighet rundt forventet levealder i kohorter og perioder. Jeg viser hvorfor det er stadig sterkere komprimering i periode-dødeligheten. Funnene er basert på periode- og kohortdødelighet fra Human Mortality Database for 10 land, inkludert Norge.
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Keilman, Nico
(2018).
Mortality shifts and mortality compression in period and cohort life tables.
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Keilman, Nico
(2018).
Increasing (but insufficient?) optimism about future life expectancy.
N-IUSSP.
Vis sammendrag
In June 2018, Statistics Norway (2018) published its latest population forecast. The agency expects that the lifespan of men will increase to 87 years in 2050. This is six years more than the current value of 81 years. In a forecast published in 1993, the life expectancy prediction for 2050 was eight years lower than in today's population forecast. For women, the life expectancy prediction for 2050 increased by "only" four and a half years - from 84.5 years in the 1993 forecast to 89 years now. Demographers and other social scientists, in Norway as well as in other developed countries, have systematically underestimated the rate of increase of the life expectancy. Governments should be concerned about stronger ageing than official forecasts suggest.
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Syse, Astri; Pham, Quang Dinh & Keilman, Nico
(2018).
Dødelighet og levealder.
I Leknes, Stefan; Løkken, Sturla Andreas Kise; Syse, Astri & Tønnessen, Marianne (Red.),
Befolkningsframskrivingene 2018: Modeller, forutsetninger og resultater.
Statistics Norway.
ISSN 978-82-537-9767-0.
s. 57–86.
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Keilman, Nico
(2018).
Psoriasispasientenes livsløp - påvirker behandling deres parforhold?
.
BestPractice Dermatologi.
9(28),
s. 18–19.
Vis sammendrag
Vår analyse viser at matematisk modellering av samspillet mellom sosiale og helsevariabler i prinsippet er mulig. Vår modell er en makromodell som simulerer livsløpet til en gruppe pasienter, som befinner seg i ulike tilstander i løpet av livet. Imidlertid trenger vi mer komplekse modeller som omfatter flere variabler (for eksempel sysselsettingsstatus, depresjon, fedme etc.) for mer realistiske simuleringer av interaksjoner som studeres. Flere variabler medfører mer komplekse simuleringsmodeller. I slike tilfeller kan det være nødvendig å bytte fra en makromodell til en mikromodell. I en slik modell simuleres livsløpet til hver enkelt person.
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Keilman, Nico
(2018).
Morgendagens eldre: større sjanse for å bo med partner og mindre sjanse for å bo alene.
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Keilman, Nico
(2017).
Population Projection in Kosovo, 2017-2061: Methodology and key findings.
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Keilman, Nico; Pham, Quang Dinh & Syse, Astri
(2017).
Mortality shifts and mortality compression in Norway 1900-2100: periods and cohorts.
Vis sammendrag
BACKGROUND:
Life expectancies and compression indicators from period life tables give a distorted picture of average life times and concentration of time of death. Cohort life tables reflect reality better.
OBJECTIVE:
To derive expressions for life expectancy and variance of age at death in period and cohort life tables
METHODS:
Focus on d_x-columns of period and cohort life tables. When radix l_0 = 1, d_x is the Age at Death Distribution (AADD).
RESULTS:
We derived translation formulae for period life expectancy and variation in period age at death as a function of the moments of the cohort AADD.
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Keilman, Nico
(2017).
Period and cohort life expectancy, mortality compression, and age at death distribution.
Vis sammendrag
We analyse the age at death distribution (AADD) of the life table, conventionally known as its d(x)-column. We derive general analytical expressions for the moments of this distribution in a period life table, written as functions of the moments in a cohort life table. The first moment is the life expectancy, while the second moment reflects compression of the age at death. The expressions are partly based on an empirical regularity that we found for Norway in observed mortality data for the years 1900-2015, and projected mortality trends until 2100.
Using the formula for the first period moment of the AADD, we derive the conditions under which cohort life expectancy increases faster than period life expectancy. We also find expressions for the period life expectancy in the year a birth cohort reaches an age equal to its own life expectancy, and for the gap between the period life expectancy in a certain year and the cohort life expectancy for the cohort born that year. Furthermore, we establish a relation between the period life expectancy in a certain year t, and the lag λ that leads to an equally large cohort life expectancy for a cohort born in year t – λ. This is the number of years it takes a period life expectancy to reach the current level of cohort life expectancy. Finally, using formulae for the second moment of the AADD, we derive expressions for lags and gaps in the standard deviations of the period and cohort AADDs. The latter measures are useful for describing trends in the compression of mortality.
Our data show that under this model, as long as cohort life expectancies are lower than 94 years of age for Norwegian men and lower than 92 years of age for Norwegian women, their cohort life expectancies will be below period life expectancies a number of years later, where the time interval equals the cohort’s life expectancy. When cohort life expectancies are higher, they will exceed the period life expectancies at this particular lag. The gap between cohort and period life expectancies will grow by roughly half a year for every one-year increase in cohort life expectancy – or by about one year of age for every period of 6-7 years. The lag λ defined above widens rapidly for Norwegian men and women, by approximately three to four years for every one-year increase of the cohort life expectancy. For women we find that compression of morbidity, as judged by the standard deviation of the AADD above age 30, went more than twice as fast in reality (i.e. in birth cohorts) than what we see by inspecting period data only.
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Keilman, Nico
(2016).
A two-sex model for first marriage.
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Keilman, Nico
(2016).
Probabilistic demographic forecasts.
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Keilman, Nico
(2016).
Barnefamilier, fruktbarhetsnivå og samfunnsplanlegging
.
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Keilman, Nico
(2016).
Befolkning: Statistisk sentralbyrå bør endre praksis og publisere sannsynlighetsprognoser.
Samfunnsøkonomen.
ISSN 1890-5250.
s. 59–66.
Vis sammendrag
Med jevne mellomrom beregner Statistisk sentralbyrå (SSB) prognoser for befolkningen fram til 2100: alderssammensetningen for hvert år, årlige antall fødsler og dødsfall, størrelsen på inn- og utvandringsstrømmene, antall innvandrere med ulik landbakgrunn, osv. Publisering av en ny prognose er planlagt for den 21. juni i år. I denne kommentaren bruker jeg resultater fra SSBs forrige befolkningsprognose fra 2014 for å gjøre rede for et viktig poeng: den framtidige befolkningsutviklingen er (som alle prognoser) usikker, men noen utviklinger er mer sannsynlige enn andre. Derfor bør SSB publisere sannsynlighetsprognoser, og ikke bare befolkningstall for framtidige år uten at brukeren vet hvor stor sjanse SSB knytter til den ene eller den andre befolkningsutviklingen.
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Keilman, Nico
(2016).
Probabilistic household forecasts for Denmark, Finland, and the Netherlands 2011-2041: Combining the Brass relational method with a Random Walk model
.
Vis sammendrag
Probabilistic household forecasts to 2041 are presented for Denmark, Finland, and the Netherlands. Future trends in fertility, mortality and international migration are taken from official population forecasts. Time series of shares of the population in six different household positions are modelled as Random Walks with a Drift. Brass’ relational model preserves the age patterns of the household shares. Probabilistic forecasts for households are computed by combining predictive distributions for the household shares with predictive distributions of the populations, specific for age and sex.
If current trends in the three countries continue, we will witness a development towards more and smaller households, often driven by increasing numbers of persons who live alone. We can be quite certain that by 2041, there will be between two and four times as many persons aged 80 and over who live alone when compared with the situation in 2011.
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Keilman, Nico
(2016).
Tar SSB høyde for den usikre demografiske utviklingen i framtiden?
.
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Bijak, Jakub; Alberts, Isabel; Alho, Juha; Bryant, John; Buettner, Thomas & Falkingham, Jane
[Vis alle 16 forfattere av denne artikkelen]
(2015).
Letter to the Editor: Probabilistic Population Forecasts for Informed Decision Making.
Journal of Official Statistics.
ISSN 0282-423X.
31(4),
s. 537–544.
doi:
10.1515/JOS-2015-0033.
Vis sammendrag
Demographic forecasts are inherently uncertain. Nevertheless, an appropriate description of this uncertainty is a key underpinning of informed decision making. In recent decades, various methods have been developed to describe the uncertainty of future populations and their structures, but the uptake of such tools amongst the practitioners of official population statistics has been lagging behind. In this letter we revisit the arguments for the practical uses of uncertainty assessments in official population forecasts, and address their implications for decision making. We discuss essential challenges, both for the forecasters and forecast users, and make recommendations for the official statistics community.
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Keilman, Nico
(2015).
Jordens befolkning, "antall munner å mette".
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Keilman, Nico
(2015).
Dimension reduction by Brass' Relational Model: Household Dynamics in Five European Countries.
Vis sammendrag
We use techniques of data dimension reduction to model and predict age patterns of household dynamics in a multi-country context. Probabilistic household forecasts are computed to 2041 for Denmark, Finland, Germany, the Netherlands and Norway. Time series of shares of the population by household position are modelled as Random Walk with Drift. Brass’ relational model preserves the age patterns of the household shares. Predictive distributions for the household shares are combined with predictive distributions of the populations. The predicted continuation of current trends towards more and smaller households is driven by increasing numbers of persons who live alone. Relative prediction uncertainty in more numerous household types is smaller than that in less numerous types.
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Keilman, Nico
(2015).
Stochastic household forecasts for Denmark, Finland, Germany, Netherlands, Norway.
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Keilman, Nico
(2014).
Probabilistic population and household forecasts.
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Keilman, Nico
(2014).
Dimension reduction of household parameter time series by the Brass logit model.
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Keilman, Nico
(2014).
Norway’s new public pension system: Is it robust against unexpected life expectancy developments?
Vis sammendrag
Norway introduced a new system for public old age pensions in January 2011. The new system leads to lower pension expenditures than the old system, because annual pension benefits under the new system are inversely proportional to the remaining life expectancy of those who retire. We can expect public pension expenditures equal to 170 billion Norwegian crowns (NOK) in 2030 and 288 billion NOK in 2050. But expenditures will be larger if retirees live longer than expected. We cannot be certain about the pace of mortality decline in the future. Therefore we have computed a probabilistic population forecast for Norway to 2050 and analysed the consequences of population growth for public old age pension expenditures. A new insight is that the new system is much less robust against unexpected longevity shocks than what was assumed earlier, in spite of the longevity adjustment. The reason is that annual pension benefits are determined when a person retires. After retirement, a retiree’s annual benefits remain the same, even when mortality changes.
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Keilman, Nico
(2013).
Pensjonsreformen kan bli mye dyrere enn mange tror.
Vis sammendrag
Det nye pensjonssystemet medfører lavere offentlige utgifter enn det gamle systemet. Statistisk sentralbyrå forventer en innsparing på 31 mrd. kroner i 2030 og 50 mrd. kroner i 2050. Hvis pensjonistenes levealder øker fortere enn forventet blir innsparingene mindre. Det er usikkert hvor høy denne levealderen kommer til å bli i fremtiden. Derfor har vi laget en sannsynlighetsprognose for befolkningsutviklingen fram til 2050 og beregnet konsekvensene for pensjonsutgifter. Vi finner at de forventede pensjonsutgifter i 2050 er langt fra sikre.
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Keilman, Nico
(2013).
Probabilistic demographic projections.
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Keilman, Nico
(2013).
Uncertainty in population projections - with special reference to the UK.
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Keilman, Nico
(2013).
"What is happening in modern demography: life course analysis, policy evaluation, dimension reduction, prediction intervals in demographic forecasting".
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Christiansen, Solveig Tobie Glestad & Keilman, Nico
(2012).
Probabilistic household forecasts based on register data: the case of Denmark and Finland.
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Keilman, Nico
(2012).
Integrated modelling of European migration flows: Methodology and main results.
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Keilman, Nico
(2012).
Missing girls in China will lead to fewer future births than previously thought.
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Keilman, Nico
(2012).
Missing girls in China will lead to fewer future births than previously thought.
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Keilman, Nico & Van Duin, Coen
(2012).
Stochastic household forecasts by coherent random shares predictions.
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Keilman, Nico
(2012).
College huishoudensprognoses.
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Keilman, Nico
(2012).
Challenges for statistics on households and families.
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Keilman, Nico
(2012).
Onzekerheid in demografische prognoses.
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Keilman, Nico
(2010).
On future households.
Vis sammendrag
We develop a method for computing probabilistic household forecasts which quantifies uncertainty in the future number of households of various types in a country. A probabilistic
household forecast helps policy makers, planners and other forecast users in the fields of housing, energy, social security etc. in taking appropriate decisions, because some household variables are more uncertain than others. Deterministic forecasts traditionally do not quantify
uncertainty. We apply the method to data from Norway.We find that predictions of future numbers of married couples, cohabiting couples and one-person households are more certain than those of lone parents and other private households. Our method builds on an existing method for computing probabilistic population forecasts, combining such a forecast with a random breakdown of the population according to household position (single, cohabiting, living with a spouse, living alone etc.). In this application, uncertainty in the total numbers of households of different types derives primarily from random shares, rather than uncertain future population size. A similar method could be applied to obtain probabilistic forecasts for other divisions of the population, such as household size, health or disability status, region of residence and labour market status.
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Keilman, Nico
(2009).
Failure and success: In-sample and out-of-sample demographic forecasts.
Vis sammendrag
There is a growing literature on the quality of population forecasts. All of these forecasts relate to the future, and their accuracy can be established ex post facto. However, a large literature is concerned with explanation and prediction of current, not future behaviour. This paper explores the predictive success of those in-sample predictions. Likelihood statistics for 96 life course analyses published in key demographic journals are investigated. To what extent does predictive success depend on subject matter, on sample size, on type of dependent variable? We find that variables for fertility/reproduction and pair formation/dissolution are difficult to explain, in particular events. For mortality and health variables the situation is somewhat better.
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Keilman, Nico
(2008).
Concern in the European Union about Low Birth Rates.
European View.
ISSN 1781-6858.
7,
s. 333–340.
doi:
10.1007/s12290-008-0055-5.
Vis sammendrag
While the European Union recognizes the importance of ageing and falling populations in a number of official documents, the findings require focus. In this article, the author first identifies the documents’ failure to name a target birth rate. Second, he stresses that the often mentioned fertility level of an average of 1.5 children per woman underestimates the real figure. Thirdly, he points out the futility of fertility policies when ageing processes will continue regardless of birth rate changes. In response, policies must be coordinated in a number of areas including gender policy, employment policy, immigration policy, housing policy, family policy and economic policy.
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Keilman, Nico & Veløy, Chris
(2008).
AFP en ulykke for landet.
[Internett].
http://nrk.no/programmer/tv/schrodingers_katt/1.6224469.
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Keilman, Nico & Veløy, Chris
(2008).
Europa kan halveres på to generasjoner.
[Internett].
http://nrk.no/programmer/tv/schrodingers_katt/1.6224278.
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Keilman, Nico
(2008).
Uncertain population forecasts.
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Keilman, Nico
(2008).
Emerging family and household types in Europe: Issues, definitions and classifications.
Vis sammendrag
Earlier work in connection with the preparation of the 2010 round of Population and Housing Censuses for member countries of the United Nations Economic Commission of Europe revealed that in the 2000 round countries experienced difficulties in collecting data for reconstituted families and same-sex couples. Other problematic issues were the distinction between private and institutional households, and the notions of de facto and legal marital status.
In addition to problems encountered in population censuses, some emerging family and household types pose problems when mapped by means of sample surveys. The UNECE Task Force on Families and Households has identified a number of additional types for which problems exist.
In this paper I shall briefly discuss the problems mentioned above, and refer to possible definitions and classifications for reconstituted families, same-sex couples and couples in a LAT-relationship (Living Apart Together).
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Keilman, Nico
(2006).
Livslängd och invandring spräckte prognoserna.
Välfärd.
ISSN 1651-6710.
s. 6–7.
Vis sammendrag
En analys av befolkningsprognosernas träffsäkerhet i 18 europeiska länder har visat att demograferna har varit för försiktiga i sina antaganden. I Europa har både medellivslängden och invandringen ökat kraftigare under de senaste decennierna än vad demograferna räknade med.
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Keilman, Nico
(2006).
European demographic forecasts have not become more accurate during the past 25 years.
Vis sammendrag
Nowadays, demographers, population statisticians, and population forecasters have richer data, more refined theories of demographic behaviour, and more sophisticated methods of analysis than they had two or three decades ago. This scientific progress should have made it easier to predict demographic behaviour. But my analyses of the errors in old forecasts show that demographic forecasts published by statistical agencies in 14 European countries have not become more accurate over the past 25 years. My findings demonstrate that scientific progress in population studies during the previous two to three decades might have been too slow to keep up with less predictable demographic behaviour of populations in European countries. There is no reason to be more optimistic about US Census Bureau forecasts. I argue that population forecasts are intrinsically uncertain, and hence should be couched in probabilistic terms.
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Alho, Juha; Cruijsen, Harri & Keilman, Nico
(2006).
Empirically-based specification of forecast uncertainty.
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Keilman, Nico & Aristotelous, Georgios
(2021).
Expert opinion on migration data. QuantMig Deliverable D6.1.
ingen.
Fulltekst i vitenarkiv
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Keilman, Nico; Pham, Quang Dinh & Syse, Astri
(2018).
Mortality shifts and mortality compression: The case of Norway, 1900-2060.
Statistics Norway.
ISSN 1892-753X.
Vis sammendrag
Historically, official Norwegian mortality projections computed by Statistics Norway have consistently under-predicted life expectancy. The projected age distribution of deaths may be used to check if the official mortality projections are plausible. The aim of the paper is to verify whether the projections predict a continuation of the ongoing compression in mortality and of the steady upward shift in the ages at which people die. We use official period data on observed (1900-2015) and projected (2016-
2060) sex- and age-specific mortality to estimate the age distribution of life table deaths. We analyse trends in life expectancy at birth, modal and median ages at death, and standard deviation of the age distribution at ages > 30.
The historical shifts towards longer longevity are projected to continue into the future. The projections suggest a steady increase in the modal and the median age at death for men and women towards values between 90 and 94 years in 2060. At present these ages are in the range 83-90 years.
Simultaneously, deaths become more concentrated around the mean, as the standard deviation of the age distribution is projected to fall continuously.
Statistics Norway’s projection methodology is capable of tracking ongoing processes of mortality shifts towards higher ages and a compression of mortality around the modal and mean ages. Mortality projections could potentially benefit from including assessments of the age distribution of
deaths.
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Kastrati, Avni; Uka, Sanije; Sojeva, Arijeta & Keilman, Nico
(2017).
Kosovo Population Projection 2017-2061.
Kosovo Agency of Statistics.
ISSN 978-9951-22-420-8.
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Keilman, Nico
(2016).
Fødsler og fruktbarhet i Norge.
Akademika AS.
Vis sammendrag
I dette vedlegget gjøres det rede for at fruktbarhetsnivået i Norge er høyere enn det Samlet fruktbarhetstall (SFT) har vist de siste årene. Samtidig er det meget sannsynlig med en økning i årlig antall fødsler fra rundt 60 000 nå til i underkant av 70 000 om ti år. En tilfredsstillende forklaring for dagens fruktbarhetsnivå er vanskelig å gi. Derfor er predikeringer av framtidens fruktbarhet usikre. En viktig konsekvens er at Statistisk sentralbyrå bør publisere sine befolkningsprognoser i form av sannsynlighetsprognoser.
Hvis vi ønsker å løse de problemene en aldrende befolkning medfører med hensyn til vårt velferdssystem, er ikke fruktbarhetsnivået av stor betydning. I denne sammenhengen er det viktig at politikere og samfunnsplanleggere vurderer tiltak innenfor arbeidsmarkedet samt skatte-, helse-, og pensjonssystemene.
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Keilman, Nico
(2015).
Probabilistic household forecasts for five countries in Europe.
ingen.
Fulltekst i vitenarkiv
Vis sammendrag
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.
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Wisniowski, Arkadiusz; Keilman, Nico; Bijak, Jakub; Christiansen, Solveig; Forster, Jonathan & Smith, Peter
[Vis alle 7 forfattere av denne artikkelen]
(2011).
Augmenting migration statistics with expert knowledge.
NORFACE.
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Graziani, Rebecca & Keilman, Nico
(2010).
The sensitivity of the Scaled Model of Error with respect to the choice of the correlation parameters: A Simulation Study.
Økonomisk institutt.
ISSN 0809-8786.
2010(22).
Vis sammendrag
Abstract: The Scaled Model of Error has gained considerable popularity during the past ten years as a device for computing probabilistic population forecasts of the cohort-component type. In this report we investigate how sensitive probabilistic population forecasts produced by means of the
Scaled Model of Error are for small changes in the correlation parameters. We consider changes in the
correlation of the age-specific fertility forecast error increments across time and age, and changes in the
correlation of the age-specific mortality forecast error increments across time, age and sex. Next we analyse the impact of such changes on the forecasts of the Total Fertility Rate and of the Male and Female Life Expectancies respectively.
For age specific fertility we find that the correlation across ages has only limited impact on the uncertainty in the Total Fertility Rate. As a consequence, annual numbers of births will be little affected. The autocorrelation in error increments is an important parameter, in particular in the long run. Also, the autocorrelation in error increments for age specific mortality is important. It has a large
effect on long run uncertainty in life expectancy values, and hence on the uncertainty around the elderly population in the future. In empirical applications of the Scaled Model of Error, one should give due attention to a correct estimation of these two parameters.
Key words: Scaled model of error, Stochastic population forecast, Probabilistic cohort component model, Sensitivity, Correlation
JEL classifications: C15, C49, C63, J41
