Nico Keilman

Peer reviewed journals

• Borgan, Ørnulf & Keilman, Nico (2018). Do Japanese and Italian Women Live Longer than Women in Scandinavia?. European Journal of Population.  ISSN 0168-6577.  s 1- 13 . doi: 10.1007/s10680-018-9468-2 Vis sammendrag
• Linder, Dennis; Frigessi, Arnoldo; Piaserico, Stefano & Keilman, Nico (2017). Simulating the life course of psoriasis patients: the interplay between therapy intervention and marital status. Journal of the European Academy of Dermatology and Venereology.
• Keilman, Nico (2016). A combined Brass-Random Walk approach to probabilistic household forecasting: Denmark, Finland, and the Netherlands 2011-2041. Journal of Population Research () 1-27. 
• Keilman, Nico (2016). Household forecasting: Preservation of age patterns. International Journal of Forecasting 32(3) 726-735.
• Keilman, Nico; Tymicki, Krzysztof & Skirbekk, Vegard (2014). Measures for human reproduction should be linked to both men and women. International Journal of Population Research.  ISSN 2090-4029.
• Arkadiusz Wiśniowski, Jakub Bijak, Solveig Christiansen, Jonathan Forster, Nico Keilman, James Raymer, Peter Smith (2013) Utilising expert opinion to improve the measurement of international migration in Europe. Journal of Official Statistics 29(4)2013, 583-607 dx.doi.org/10.2478/jos-2013-0041
• Solveig Christiansen and Nico Keilman (2013) Probabilistic household forecasts based on register data - the case of Denmark and Finland. Demographic Research 28, 1263-1302. 10.4054/DemRes.2013.28.43
• Cohen, Joel; Kravdal, Øystein & Keilman, Nico (2011). Childbearing impeded education more than education impeded childbearing among Norwegian women. Proceedings of the National Academy of Science of the United States of America.  ISSN 0027-8424.  108(29), s 11830- 11835 . doi: 10.1073/pnas.1107993108
• Juha Alho and Nico Keilman, On future household structure. Journal of the Royal Statistical Society Series A 173(1)2010, 117-143
• Nico Keilman and Solveig Christiansen, Norwegian elderly less likely to live alone in the future. European Journal of Population 26 2010, DOI 10.1007/s10680-009-9195-9
• Nico Keilman, Concern in the European Union about Low Birth Rates, European View 2008
• Nico Keilman, European demographic forecasts have not become more accurate during the past 25 years, Population and Development Review, 2008, 34(1)
• Maarten Alders, Nico Keilman and Harri Cruijsen, Assumptions for long-term stochastic population forecasts in 18 European countries, European Journal of Population, 2007, 23:33-69
• Juha Alho, Maarten Alders, Harri Cruijsen, Nico Keilman, Timo Nikander and Dinh Quang Pham, New forecast: Population decline postponed in Europe, Statistical Journal of the United Nations ECE12, 2006, 23:1-10
• Nico Keilman, The Impact of Demographic Uncertainty on Liabilities for Public Old Age Pensions in Norway, New Zealand Population Review 31(1), 2005, 35-50
• Nico Keilman and Dinh Quang Pham, Time series based errors and empirical errors in fertility forecasts in the Nordic countries, International Statistical Review 72(1), 2004, 5-18, summary
• Nico Keilman, Biodiversity: The threat of small households, Nature 421, 30 January 2003, 489-490
• Nico Keilman, Dinh Quang Pham and Arve Hetland, Why population forecasts should be probabilistic - illustrated by the case of Norway, Demographic Research 6-15 May 2002, 409-454
• Nico Keilman, TFR predictions and Brownian motion theory, Yearbook of Population Research in Finland 38, 2002, 209-221
• Nico Keilman, Data quality and accuracy of United Nations population projections, 1950-95, Population Studies 55(2), 2001, 149-164
• Nico Keilman, Uncertain population forecasts, Nature 412, 6846, 2001, 490-491
• Nico Keilman and Dinh Quang Pham, Predictive intervals for age-specific fertility, European Journal of Population 16(1), 2000, 41-66
• Nico Keilman, How accurate are the United Nations' world population projections?,Population and Development Review supplement to Volume 24, 1998, 15-41

Working papers

• Nico Keilman and Dinh Quang Pham, Empirical errors and predicted errors in fertility, mortality and migration forecasts in the European Economic Area, Oslo: Statistics Norway, 2004
• Nico Keilman, Demographic implications of low fertility for family structures in Europe, Strasbourg: Council of Europe, 2003
• Nico Keilman, Children and time: The Norwegian model, NIDI Hofstee Lectures Series nr. 6, Netherlands Interdisciplinary Demographic Institute, 2001.

Books

• N. Keilman, J. Lyngstad, H. Bojer, and I. Thomsen (eds.) Poverty and Economic Inequality in Industrialized Western Societies. Oslo: Scandinavian University Press, 1998, 334 pp. http://urn.nb.no/URN:NBN:no-nb_digibok_2010052503035
• J.-P. Gonnot, N. Keilman and C. Prinz, Social Security, Household and Family Dynamics in Ageing Societies. Dordrecht: Kluwer Academic Publishers, 1995, 235 pp.
• E. van Imhoff and N. Keilman, LIPRO 2.0: An Application of a Dynamic Demographic Projection Model to Household Structure in the Netherlands. Amsterdam and Berwyn, PA: Swets and Zeitlinger Publishers, 1991, 239 pp.
• N. Keilman, Uncertainty in National Population Forecasting: Issues, Backgrounds, Analyses, Recommendations. Amsterdam and Rockland, MA: Swets and Zeitlinger Publishers, 1990, 211 pp.
•  N. Keilman, A. Kuijsten, and A. Vossen (eds.), Modelling Household Formation and Dissolution. Oxford: Clarendon Press, 1988, 298 pp.

• Bengtsson, Tommy; Keilman, Nico; Alho, Juha; Christensen, Kaare; Palmer, Edward & Vaupel, James W. (2019). Introduction, In Tommy Bengtsson & Nico Keilman (ed.),  Old and New Perspectives on Mortality Forecasting.  Springer Nature.  ISBN 978-3-030-05074-0.  Chapter 1.  s 1 - 19
• Keilman, Nico (2019). Family projection methods: A review, In Robert Schoen (ed.),  Analytical Family Demography.  Springer Nature.  ISBN 978-3-319-93227-9.  12.  s 277 - 301
• Borgan, Ørnulf & Keilman, Nico (2018). Do Japanese and Italian Women Live Longer than Women in Scandinavia?. European Journal of Population.  ISSN 0168-6577.  s 1- 13 . doi: 10.1007/s10680-018-9468-2 Vis sammendrag

  Life expectancies at birth are routinely computed from period life tables. When mortality is falling, such period life expectancies will typically underestimate real life expectancies, that is, life expectancies for birth cohorts. Hence, it becomes problematic to compare period life expectancies between countries when they have different historical mortality developments. For instance, life expectancies for countries in which the longevity improved early (like Norway and Sweden) are difficult to compare with those in countries where it improved later (like Italy and Japan). To get a fair comparison between the countries, one should consider cohort data. Since cohort life expectancies can only be computed for cohorts that were born more than a hundred years ago, in this paper we suggest that for younger cohorts one may consider the expected number of years lost up to a given age. Contrary to the results based on period data, our cohort results then indicate that Italian women may expect to lose more years than women in Norway and Sweden, while there are no indications that Japanese women will lose fewer years than women in Scandinavia. The large differences seen for period data may just be an artefact due to the distortion that period life tables imply in times of changing mortality.

• Linder, Dennis; Frigessi, Arnoldo; Piaserico, Stefano & Keilman, Nico (2017). Simulating the life course of psoriasis patients: the interplay between therapy intervention and marital status. Journal of the European Academy of Dermatology and Venereology.  ISSN 0926-9959.  32(1), s 62- 67 . doi: 10.1111/jdv.14567 Fulltekst i vitenarkiv. Vis sammendrag

  Background Psoriasis, a chronic relapsing inflammatory disease affecting primarily the skin, shows multiple comorbidities including depression, cardiovascular diseases and other relevant conditions. Psoriasis patients experience social isolation, job loss, financial difficulties and partnership problems. Inversely, psychosocial impairments may negatively influence the disease course. Objective To explore the feasibility of a model describing the interaction of psychosocial and clinical factors over the life course of patients. Methods We considered only seven states for members of a hypothetical population: single and healthy, single and having a psoriasis flare, single and ‘cured’, coupled and healthy, coupled and having a psoriasis flare, coupled and ‘cured’, and dead. Transition probabilities between states were taken from the Norwegian Population Register for the healthy population and from epidemiological research articles. Clinical experience allowed adjustments on the assumed parameters. Results Our macromodel, which simulates the effect of therapy intervention on patients’ partnership status, yields a description of the transitions between the seven states. Treatment efficacy shows only a negligible effect on the chances of living with a partner. Conclusions Mathematical modelling of interactions between social and health variables is in principle feasible. However, complex models, comprising more variables (for instance: employment status, depression level, obesity etc.), are needed for more realistic simulations for the interactions studied. As increasing the number of variables leads to an exponential increase of the model’s state space, switching to micromodelling (representing each individual separately) may be necessary.

• Keilman, Nico (2016). A combined Brass-random walk approach to probabilistic household forecasting: Denmark, Finland, and the Netherlands, 2011–2041. Journal of Population Research.  ISSN 1443-2447.  34(1), s 17- 43 . doi: 10.1007/s12546-016-9175-y 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 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.

• Keilman, Nico (2016). Household forecasting: Preservation of age patterns. International Journal of Forecasting.  ISSN 0169-2070.  32(3), s 726- 735 . doi: 10.1016/j.ijforecast.2015.10.007 Vis sammendrag

  We formulate a time series model for household dynamics for different age groups. We model the share of the population who are in a certain household position (living alone, living with partner etc.). These household positions have very pronounced age patterns. The age profiles change slowly over time, caused by changes in home leaving behaviour of young adults, differences in survival of men and women, etc. When forecasting household positions to 2040, we want to preserve the characteristics of the age profiles. We test the Lee-Carter model and the Brass Relational method using household data for the Netherlands for the period 1996-2010. Annual shares of the population by household position, age, and sex are modeled as Random Walks with a Drift (RWD). While the Brass model has its limitations, in our application it performs better than the Lee-Carter model. The predicted age patterns based on the Brass model look more reasonable, because the Brass model is a two-parameter model, while the Lee-Carter model contains only one parameter. Also, the model parameters and standard errors of the Brass model are easier to estimate than those of the Lee-Carter model.

• Keilman, Nico; Tymicki, Krzysztof & Skirbekk, Vegard (2014). Measures for human reproduction should be linked to both men and women. International Journal of Population Research.  ISSN 2090-4029. . doi: 10.1155/2014/908385 Fulltekst i vitenarkiv. Vis sammendrag

  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.

• Christiansen, Solveig Tobie Glestad & Keilman, Nico (2013). Probabilistic household forecasts based on register data - the case of Denmark and Finland. Demographic Research.  ISSN 1435-9871.  28, s 1263- 1302 . doi: 10.4054/DemRes.2013.28.43 Vis sammendrag

  BACKGROUND Household forecasts are important for public planning and for predicting consumer demand. OBJECTIVE The purpose of this paper is to compute probabilistic household forecasts for Finland and Denmark, taking advantage of unique housing register data covering the whole populations dating back to the 1980s. A major advantage is that we do not have to rely on small population samples, and we can get quite reliable estimates even for infrequent transitions. A further merit is having time series containing the population in different household positions (dependent child, living with a spouse, living in a consensual union, living alone, lone parent, living in other private household and institutional households) by age and sex. METHODS These series enable us to estimate the uncertainty in the future distribution of the population across household positions. Combining these uncertainty parameters with expected shares computed in a deterministic household forecast, we simulate 3000 sample paths for the household shares for each age and sex. These paths are then combined with 3000 simulations from a stochastic population forecast covering the same period to obtain the predicted number of households and persons in each household position by age and sex. RESULTS According to our forecasts, we expect a strong growth in the number of private households during a 30-year period, of 27% in Finland and 13% in Denmark. The number of households consisting of a married couple or a person who lives alone are the most certain, and single parents and other private households are the most uncertain.

• Keilman, Nico & Keller, Lisa Dahl (2013). Hvor robust er det nye pensjonssystemet med hensyn til levealdersutviklingen?. Samfunnsøkonomen.  ISSN 1890-5250.  (6), s 28- 38
• Wisniowski, Arkadiusz; Bijak, Jakub; Christiansen, Solveig Tobie Glestad; Forster, Jonathan; Keilman, Nico; Raymer, James & Smith, Peter (2013). Utilising Expert Opinion to Improve the Measurement of International Migration in Europe. Journal of Official Statistics.  ISSN 0282-423X.  29(4), s 583- 607 . doi: 10.2478/jos-2013-0041
• Cohen, Joel; Kravdal, Øystein & Keilman, Nico (2011). Childbearing impeded education more than education impeded childbearing among Norwegian women. Proceedings of the National Academy of Sciences of the United States of America.  ISSN 0027-8424.  108(29), s 11830- 11835 . doi: 10.1073/pnas.1107993108
• Alho, Juha & Keilman, Nico (2010). On future household structure. Journal of the Royal Statistical Society: Series A (Statistics in Society).  ISSN 0964-1998.  173, s 117- 143 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. Keywords: Household forecast; Norway; Population forecast; Probabilistic forecast; Random shares

• Keilman, Nico (2010). On age structures and mortality, In Shripad Tuljapurkar; Naohiro Ogawa & Anne Gauthier (ed.),  Ageing in Advanced Industrial States.  Springer.  ISBN 978-90-481-3552-3.  kap 2.  s 23 - 46
• Keilman, Nico & Christiansen, Solveig Tobie Glestad (2010). Norwegian Elderly Less Likely to Live Alone in the Future. European Journal of Population.  ISSN 0168-6577.  26(1), s 47- 72 . doi: 10.1007/s10680-009-9195-9 Vis sammendrag

  We analyse the future household status of elderly men and women in Norway. An important finding is that persons aged 80+ are less likely to live alone in the future, and more often with a partner. The level of mortality, the mortality sex gap, union dissolution at young and intermediate ages, and entry into and exit from institutions for the elderly are possible determinants for this new trend. We use the macro simulation program LIPRO to simulate the household dynamics in Norway to 2032, and investigate the demographic reasons for the increased likelihood of living with a partner among Norwegian elderly. Mortality plays an important role, but part of the trend is already embodied in the household structure of the current population. Keywords: Elderly, Living arrangements, Household projection, Norway, LIPRO

• Alho, Juha; Cruijsen, Harri & Keilman, Nico (2008). Empirically based specification of forecast uncertainty, In Juha Alho; Svend Hougaard Jensen & Jukka Lassila (ed.),  Uncertain demographics and fiscal sustainability.  Cambridge University Press.  ISBN 978-0-521-87740-4.  kap. 3.  s 34 - 54
• Keilman, Nico (2008). Erroneous population forecasts, I: Patrick Festy & Jean-Paul Sardon (red.),  Hommage à Gérard Calot - Profession: démographe.  Institut national d'études démographiques.  ISBN 978-2-7332-4023-6.  -.  s 237 - 254
• Keilman, Nico (2008). European demographic forecasts have not become more accurate over the past 25 years. Population and Development Review.  ISSN 0098-7921.  34(1), s 137- 153 Vis sammendrag

  Nowadays, demographers, population statisticians, and population forecasters have richer data, more refined theories of demographic behavior, 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 behavior. But my analysis of the errors in old forecasts shows that demographic forecasts published by statistical agencies in 14 European countries have not become more accurate over the past 25 years. The findings demonstrate that scientific progress in population studies during the previous two to three decades has not kept up with less predictable demographic behavior of populations in European countries. There is no reason to be more optimistic about US Census Bureau forecasts. Population forecasts are intrinsically uncertain, and hence should be couched in probabilistic terms.

• Keilman, Nico; Cruijsen, Harri & Alho, Juha (2008). Changing views of future demographic trends, In Juha Alho; Svend Hougaard Jensen & Jukka Lassila (ed.),  Uncertain demographics and fiscal sustainability.  Cambridge University Press.  ISBN 978-0-521-87740-4.  kap. 2.  s 11 - 33
• Alders, Maarten; Keilman, Nico & Cruijsen, Harri (2007). Assumptions for long-term stochastic population forecasts in 18 European countries. European Journal of Population.  ISSN 0168-6577.  23, s 33- 69 . doi: 10.1007/s10680-006-9104-4 Vis sammendrag

  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.

• Keilman, Nico (2007). UK national population projections in perspective: How successful compared to those in other European countries?. Population Trends.  ISSN 0307-4463.  (129), s 20- 30 Vis sammendrag

  Compared to population forecasts of other European countries, those made in the United Kingdom during the past 30 years had somewhat larger forecast errors for fertility and smaller errors for mortality. Migration forecasts in the UK were about as accurate as the European average. After controlling for various effects such as relative data volatility both at the time a projection is made and during the period of the projection, there is no indication that recent forecasts in European countries have been more accurate than older ones. Hence population forecasts are intrinsically uncertain, and a forecast for the UK in the form of probability distributions is presented.

• Alho, Juha; Alders, Maarten; Cruijsen, Harri; Keilman, Nico; Nikander, Timo & Pham, Dinh Quang (2006). New forecast: Population decline postponed in Europe. Statistical Journal of the United Nations Economic Commision for Europe ECE12.  ISSN 0167-8000.  23(1), s 1- 10 Vis sammendrag

  We present results of a probabilistic forecast for the population in 18 European countries, to 2050. Other forecasts have recently predicted a falling population size for those countries. However, there are reasons to expect higher immigration and lower mortality than the earlier forecasts did. Hence, we find that population decline is postponed in our forecast. The forecast also alerts us to the fact that many demographic developments cannot be forecasted accurately. Although ageing is certain, the extent to which this will occur is difficult to predict accurately. The number of elderly persons is very uncertain in the long run. This has major implications for all European countries in which reforms for pension systems and the provision of health care for the elderly are considered. The reforms must be robust against unexpected demographic developments.

• Keilman, Nico (2006). Demographic Translation: From Period to Cohort Perspective and Back, In Guillaume Wunsch; Graziella Caselli & Jacques Vallin (ed.),  Demography: Analysis and Synthesis, A Treatise in Population Studies Volume 1.  Elsevier.  ISBN 978-0-12-765660-1.  Chapter 17.  s 213 - 223
• Keilman, Nico (2006). Households and families, In Guillaume Wunsch; Graziella Caselli & Jacques Vallin (ed.),  Demography: Analysis and Synthesis.  Academic Press.  ISBN 0-12-765664-2.  Ch. 91 in Vol. 3.  s 457 - 476 Vis sammendrag

  The aim of the current chapter is to give not only a broad overview of trends in family and household developments in Europe (Section 5), but also to discuss concepts and definitions concerning the household, the family, and their members (Section 2): what constitutes a household, a family, what is a consensual union, a child, a one-parent family, a reconstituted family? Next I review various issues connected to measuring household and family developments (Section 3): de facto or de jure place of residence, measuring household and family structure at one point in time, measuring household and family dynamics over a certain period, the individual or the group as unit of measurement, the problem of longitudinal households, relationships between the events that several members of the same household may experience (e.g. a lone mother becomes a one-person household when her last child leaves the parental home), and the representativeness of the data. The strengths and weaknesses of various data sources often used to map household and family developments are discussed in Section 4. Finally, the main trends in family and household developments in Europe after World War II are summarized in Section 5.

• Alho, Juha; Alders, Maarten; Cruijsen, Harri; Keilman, Nico; Nikander, Timo & Pham, Dinh Quang (2005). Befolkningsnedgang i EØS-området utsatt. SSB-Magasinet. Vis sammendrag

  FN og Eurostat har nylig spådd en befolkningsnedgang for 18 europeiske land. Nå viser resultatene fra en ny befolkningsprognose at vi har grunn til å vente større innvandring og høyere levealder enn det som FN og Eurostat har spådd. Nedgangen kommer derfor senere enn antatt, kanskje ikke i det hele tatt før 2050. For Norge forventes det et folketall på 5,75 millioner i 2050, litt høyere enn midtalternativet i SSBs befolkningsframskrivninger fra 2002.

• Keilman, Nico (2005). Erroneous population forecasts, In Nico Keilman (ed.),  Perspectives on Mortality Forecasting No. 2: Probabilistic Models.  Swedish Social Insurance Agency.  ISBN 91-7500-326-0.  kap. 1.  s 7 - 26 Vis sammendrag

  The purpose of this chapter is to give a broad review of the notions of population forecast errors and forecast accuracy. Why are population forecasts inaccurate? How large are the errors involved, when we analyse historical forecasts of fertility, mortality, and the age structure? Moreover, how can we compute expected errors in recent forecasts? We shall see that probabilistic population forecasts are necessary to assess the expected accuracy of a forecast, and that such probabilistic forecasts quantify expected accuracy and expected forecast errors much better than traditional deterministic forecasts do. The chapter concludes with some challenges in the field of probabilistic population forecasting.

• Keilman, Nico (2005). The Impact of Demographic Uncertainty on Liabilities for Public Old Age Pensions in Norway. New Zealand Population Review.  ISSN 0111-199X.  31(1), s 35- 50 Vis sammendrag

  The paper analyses the importance of demographic uncertainty for the net present value (NPV) of public old age pension obligations in Norway. A probabilistic population forecast is combined with a deterministic macro model for future pension expenditures. The model is applied to Norwegian data for the period 2003-2100. Under the current pension system, the liabilities are likely to grow by a factor of ten towards the end of the century. The demographic driving force is an assumed increase in the life expectancies of men and women by some 13-16 years over the next 95 years. The results show also that long-run relative uncertainty is larger for total population than for the NPV, due to the enormous uncertainty in the number of births in the long run. In 2100, the 80 per cent prediction interval of population size is 1.5 times as wide as the median value. For the NPV, this relative uncertainty ratio is 80 per cent. Also for earlier years, the relative uncertainty in the NPV is approximately half that in population size. There is no single broad age group, in which relative uncertainty is similar to that in the NPV during the entire period.

• Keilman, Nico & Pham, Dinh Quang (2005). Hvor lenge kommer vi til å leve? Levealder og aldersmønster for dødeligheten i Norge, 1900–2060. Økonomiske analyser.  ISSN 0800-4110.  (6), s 43- 49 Vis sammendrag

  Vi presenterer en analyse av levealderen og aldersmønsteret i dødeligheten for norske menn og kvinner siden 1900. Vi har brukt den såkalte Lee-Cartermodellen for å kunne gi anslag på dødelighetsutviklingen i framtiden. Vi forventer en fortsatt økning i levealderen, helt opp til 86 år for menn og 90 år for kvinner i 2060.

• Keilman, Nico Willem (2004). Démographie des ménages et de la famille: Application aux pays développés, I: Graziella Caselli; Jacques Vallin & Guillaume Wunsch (red.),  Démographie: Analyse et synthèse. Volume VI: Population et société.  Institut National d'Etudes Démographiques.  ISBN 27332-2016-0.  Chapitre 91.  s 345 - 388
• Keilman, Nico Willem & Quang Pham, Dinh (2004). Time series based errors and empirical errors in fertility forecasts in the Nordic countries. International Statistical Review.  ISSN 0306-7734.  72, s 5- 18 Vis sammendrag

  We use time series models to derive model based prediction intervals for the Total Fertility Rate (TFR) in Norway, Sweden, Finland, and Denmark up to 2050. For the short term (5-10 yrs), expected TFR-errors are compared with empirical forecast errors observed in historical population forecasts prepared by the statistical agencies in these countries since 1969. Medium-term and long-term (up to 50 years) errors are compared with error patterns based on so-called naïve forecasts, i.e. forecasts that assume that recently observed TFR-levels also apply for the future.

Se alle arbeider i Cristin

• Bengtsson, Tommy & Keilman, Nico (ed.) (2019). Old and New Perspectives on Mortality Forecasting. Springer Nature.  ISBN 978-3-030-05074-0.  350 s. 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.

• Keilman, Nico (ed.) (2005). Perspectives on Mortality Forecasting No. 2: Probabilistic Models. Swedish Social Insurance Agency.  ISBN 91-7500-326-0.  79 s. Vis sammendrag

  This is the second volume in a series presenting papers from workshops on mortality organized by the Stockholm Committee on Mortality Forecasting. The chapters focus on probabilistic (also labeled as stochastic) forecasts, in other words forecasts in which uncertainty has been quantified. Given a history of sizable forecasting errors, the first paper, by Nico Keilman, addresses the question of why demographic forecasts are uncertain. In the second paper Juha Alho outlines the statistical background of uncertain events and forecasts of these. In the third paper Maarten Alders and Joop de Beer sketch the approach taken by Statistics Netherlands in their stochastic forecast of mortality, while in the fourth paper Shripad Tuljapurkar presents a model for mortality analysis and forecasting that has proven to be feasible for probabilistic forecasts. He gives illustrations of US and Swedish mortality, and discusses also possible implications of uncertain mortality for future pension expenditures.

• Bengtsson, Tommy & Keilman, Nico Willem (ed.) (2003). Perspectives on Mortality Forecasting. Social Insurance Board.  ISBN 91-89303-24-5.  97 s. Vis sammendrag

  Mortality projections are an essential input for projections of the financial development of pension schemes. Governments and insurance companies all over the world rely on good mortality projections for efficient administration of their pension commitments. But the historical record shows that, during recent decades, demographers have continually underestimated improvements in life expectancy for persons 60 and older. The demographic models used in projecting mortality are usually based on statistical modeling of historical data. The question is, is it possible to improve forecast accuracy of mortality models, and if so, what do demographers need to do to achieve this result? This is the question that provided the impetus for forming the Stockholm Committee on Mortality Forecasting. It brings together a broad range of disciplines, and the work will be reported in the series �Social Insurance Studies�. The committee�s first volume addresses general issues. Why are we interested in improving mortality projections? Which forecast models are in use? How can we forecast life expectancy? How would an epidemiologist improve existing models? How can we use historical experience in getting forecasting right? Which projection models are currently in use in Finland, Norway, and Sweden? Key words: mortality forecasting, pensions, social insurance, statistics, epidemiology, history, Nordic countries

Se alle arbeider i Cristin

• Keilman, Nico (2019). Erroneous population forecasts, In Tommy Bengtsson & Nico Keilman (ed.),  Old and New Perspectives on Mortality Forecasting.  Springer Nature.  ISBN 978-3-030-05074-0.  Chapter.  s 95 - 111 Vis sammendrag

  Given a history of sizable forecasting errors, this chapter addresses the question of why demographic forecasts are uncertain. It summarizes main patterns in ex-post observed errors in historical population forecasts, and discusses probabilistic forecasts. Illustrations come from a probabilistic forecast for the population of Norway.

• Keilman, Nico (2019). Probabilistic household and living arrangement forecasts. Vis sammendrag

  First, we discuss the shortcomings of deterministic forecasting models, and argue why probabilistic household and living arrangement forecasts are necessary for informed decision-making. Information from probabilistic forecasts allows policy makers, planners, and other forecast users in the fields of housing, energy, social security etc. to take appropriate decisions, because some household variables are more difficult to predict, and hence more uncertain, than others. It also guides them once actual developments start to deviate from the most likely path. New actions or updated plans are unnecessary as long as developments are likely to remain close to the expected future. Next, we review probabilistic household forecasts published since the turn of the century. An important issue is how to evaluate, ex-post facto, the accuracy of these probabilistic forecasts. We introduce the notion of a scoring function, the general idea of which is that a forecast that predicts the actual outcome with high probability should receive a better score than one that predicts the same outcome with lower probability. Scoring functions are useful for evaluating both interval forecasts (given in the form of prediction intervals with pre-specified coverage probability), and forecasts available in the form of a full probability distribution, either analytically or as a sample. Finally, we give empirical results for scoring functions applied to the first known probabilistic household forecasts.

• Keilman, Nico (2019). Probabilistic household and living arrangement forecasts. Vis sammendrag

  First, we discuss the shortcomings of deterministic forecasting models, and argue why probabilistic household and living arrangement forecasts are necessary for informed decision-making. Probabilistic forecasts inform the user of the uncertainty around expected household trends. As opposed to a deterministic forecast, which predicts only a few numbers (when projection variants are used) for a certain year, a probabilistic forecast tells us how likely it is that the number of households for a given year in the future will be within a certain range. Information of this kind allows policy makers, planners, and other forecast users in the fields of housing, energy, social security etc. to take appropriate decisions, because some household variables are more difficult to predict, and hence more uncertain, than others. It also guides them once actual developments start to deviate from the most likely path. New actions or updated plans are unnecessary as long as developments are likely to remain close to the expected future. Next, we review probabilistic household forecasts published since the turn of the century. An important issue is how to evaluate, ex-post facto, the accuracy of these probabilistic forecasts. We introduce the notion of a scoring function, the general idea of which is that a forecast that predicts the actual outcome with high probability should receive a better score than one that predicts the same outcome with lower probability. Scoring functions are useful for evaluating both interval forecasts (given in the form of prediction intervals with pre-specified coverage probability), and forecasts available in the form of a full probability distribution, either analytically or as a sample. Finally, we give empirical results for scoring functions applied to the first known probabilistic household forecasts.

• Keilman, Nico (2018). Eldrebølgen avlyst? Bedre termometer, men pasienten er fortsatt syk. Forskning.no.  ISSN 1891-635X.
• 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.

• 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.

• Keilman, Nico (2018). Morgendagens eldre: større sjanse for å bo med partner og mindre sjanse for å bo alene.
• Keilman, Nico (2018). Mortality shifts and mortality compression in period and cohort life tables.
• Keilman, Nico (2018). Psoriasispasientenes livsløp - påvirker behandling deres parforhold?. BestPractice Dermatologi.  9(28), s 18- 19 . doi: https://www.epaper.dk/bpnoderma/derma/derma-28-no_feb_2018/?offline=1 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.

• Keilman, Nico (2018). Training Course on Demographic Analysis and Population Projections, Pristina, Kosovo, 4-7 December 2018.
• Keilman, Nico (2018). Økende optimisme om forventet levealder. Forskning.no.  ISSN 1891-635X. . doi: https://forskning.no/aldring-statistikk-helse/okende-optimisme-om-forventet-levealder/1213385
• Keilman, Nico; Pham, Dinh Quang & Syse, Astri (2018). Mortality shifts and mortality compression: The case of Norway, 1900-2060. Discussion papers. 884. 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.

• Syse, Astri; Pham, Dinh Quang & Keilman, Nico (2018). Dødelighet og levealder, I: Stefan Leknes; Sturla Andreas Kise Løkken; Astri Syse & Marianne Tønnessen (red.),  Befolkningsframskrivingene 2018: Modeller, forutsetninger og resultater.  Statistics Norway.  ISBN 978-82-537-9767-0.  Chapter 5.  s 57 - 86
• Kastrati, Avni; Uka, Sanije; Sojeva, Arijeta & Keilman, Nico (2017). Kosovo Population Projection 2017-2061.
• 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.

• Keilman, Nico (2017). Population Projection in Kosovo, 2017-2061: Methodology and key findings.
• Keilman, Nico; Pham, Dinh Quang & 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.

• Bernhoft, Aksel; Albihn, Ann; Hessen, Dag Olav; Holmboe-Ottesen, Gerd; Keilman, Nico; Børresen, Trond; Haug, Ruth; Nicolaysen, Anna Marie & Andersen, Regine (2016). Hvordan skal vi sikre et landbruk som kan fø oss i framtiden?. Vis sammendrag

  Menneskenes endring av naturen og jordas ressurser truer framtidens landbruk. Så hvordan skal den sårbare matproduksjonen best foregå på lang sikt når jordas befolkning øker?

• Keilman, Nico (2016). A two-sex model for first marriage.
• Keilman, Nico (2016). Barnefamilier, fruktbarhetsnivå og samfunnsplanlegging.
• Keilman, Nico (2016). Befolkning: Statistisk sentralbyrå bør endre praksis og publisere sannsynlighetsprognoser. Samfunnsøkonomen.  ISSN 1890-5250.  (3), 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.

• Keilman, Nico (2016). Fødsler og fruktbarhet i Norge. 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.

• Keilman, Nico (2016). Probabilistic demographic forecasts.
• 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.

• Keilman, Nico (2016). Tar SSB høyde for den usikre demografiske utviklingen i framtiden?.
• Bijak, Jakub; Alberts, Isabel; Alho, Juha; Bryant, John; Buettner, Thomas; Falkingham, Jane; Forster, Jonathan; Gerland, Patrick; King, Thomas; Onorante, Luca; Keilman, Nico; O'Hagan, Anthony; Owens, Darragh; Raftery, Adrian E.; Ševcikova, Hana & Smith, Peter (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.

• 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.

• Keilman, Nico (2015). Jordens befolkning, "antall munner å mette".
• Keilman, Nico (2015). Probabilistic household forecasts for five countries in Europe. 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.

• Keilman, Nico (2015). Stochastic household forecasts for Denmark, Finland, Germany, Netherlands, Norway.
• Keilman, Nico (2015). Stochastic household forecasts for five countries in Europe.
• Keilman, Nico (2014). Dimension reduction of household parameter time series by the Brass logit model.
• 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.

• Keilman, Nico (2014). Probabilistic population and household forecasts.
• 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.

• Keilman, Nico (2013). Probabilistic demographic projections.
• Keilman, Nico (2013). Uncertainty in population projections - with special reference to the UK.
• Keilman, Nico (2013). "What is happening in modern demography: life course analysis, policy evaluation, dimension reduction, prediction intervals in demographic forecasting".
• Christiansen, Solveig Tobie Glestad & Keilman, Nico (2012). Probabilistic household forecasts based on register data: the case of Denmark and Finland.
• Keilman, Nico (2012). Challenges for statistics on households and families.
• Keilman, Nico (2012). College huishoudensprognoses.
• Keilman, Nico (2012). Integrated modelling of European migration flows: Methodology and main results.
• Keilman, Nico (2012). Missing girls in China will lead to fewer future births than previously thought.
• Keilman, Nico (2012). Missing girls in China will lead to fewer future births than previously thought.
• Keilman, Nico (2012). Onzekerheid in demografische prognoses.
• Keilman, Nico & Van Duin, Coen (2012). Stochastic household forecasts by coherent random shares predictions.
• Wisniowski, Arkadiusz; Keilman, Nico; Bijak, Jakub; Christiansen, Solveig; Forster, Jonathan; Smith, Peter & Raymer, James (2011). Augmenting migration statistics with expert knowledge.
• 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. Memorandum from Department of Economics, University of Oslo. 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

• 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.

• 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.

• 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.

• 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).

• Keilman, Nico (2008). Scenarios of demographic change: the role of probabilistic approaches in population projection. Vis sammendrag

  This brief note first summarizes the four main demographic trends that are expected for the coming decades, and discusses two techniques that demographers use for mapping future population trends, namely scenarios and probabilistic projections.

• Keilman, Nico (2008). Uncertain population forecasts.
• Keilman, Nico (2008). Using deterministic and probabilistic population forecasts. Interdisciplinary Communications.  ISSN 0809-8735.
• Keilman, Nico & Veløy, Chris (2008, 17. september). AFP en ulykke for landet. [Internett].  http://nrk.no/programmer/tv/schrodingers_katt/1.6224469.
• Keilman, Nico & Veløy, Chris (2008, 17. september). Europa kan halveres på to generasjoner. [Internett].  http://nrk.no/programmer/tv/schrodingers_katt/1.6224278.
• Alho, Juha; Cruijsen, Harri & Keilman, Nico (2006). Empirically-based specification of forecast uncertainty.
• 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.

• Keilman, Nico (2006). Livslängd och invandring spräckte prognoserna. Välfärd.  ISSN 1651-6710.  (3), 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.

• Keilman, Nico; Cruijsen, Harri & Alho, Juha (2006). Diverging views of future demographic trends.
• Alders, Maarten; Keilman, Nico & Cruijsen, Harri (2005). Assumptions for national stochastic population forecasts in 18 European countries. Vis sammendrag

  In order to develop and implement stochastic population forecasting for European countries, the European Commission has commissioned the project �Changing population of Europe: uncertain future� to researchers from Joensuu University and the statistical offices of Finland, the Netherlands and Norway. The objective was to develop a general methodology for assessing predictive distributions for fertility, mortality and migration. Generally speaking the assumptions underlying stochastic population forecasts can be assessed by means of analyses of errors of past forecasts, model-based estimates of forecast errors, and expert judgement. All these approaches have been thoroughly investigated in the project. The paper summarises and discusses the results of the different approaches. It demonstrates how the, sometimes conflicting, results can be synthesised into a consistent set of assumptions. Finally, the paper presents the main assumptions about the total fertility rate, life expectancy at birth of men and women, and net migration for 18 European countries.

• Alho, Juha; Alders, Maarten; Cruijsen, Harri; Keilman, Nico; Nikander, Timo & Pham, Dinh Quang (2005). Population decline postponed in Europe. Statistical Magazine. Vis sammendrag

  The United Nations and Eurostat recently predicted that the population in 18 European countries will fall in the future. However, the results of a new forecast show that there is reason to expect higher immigration and lower mortality than that predicted by the UN and Eurostat. Hence, the population decline will happen later, and perhaps as late as 2050. For Norway, a population of 5.75 million is anticipated by 2050, slightly higher than the official forecast by Statistics Norway in 2002.

• Keilman, Nico (2004). Long term public old age pension obligations in a stochastic demographic setting in Norway.
• Keilman, Nico Willem (2004). Conséquences démographiques et sociales d'une faible fécondité pour les structures familiales en Europe.
• Keilman, Nico Willem & Pham, Dinh Q. (2004). Empirical errors and predicted errors in fertility, mortality and migration forecasts in the European Economic Area. Vis sammendrag

  We analyse empirical errors observed in historical population forecasts produced by statistical agencies in 14 European countries since 1950. The focus is on forecasts for three demographic variables: fertility (Total Fertility Rate - TFR), mortality (life expectancy at birth), and migration (net migration). We inspect forecast bias and forecast accuracy in the historical forecasts, as well as the distribution of the errors. Finally, we analyse for each of the three variables correlation patterns in forecast errors across countries and, for mortality, the correlation between errors for men and women. In the second part of the report we use time series model to construct prediction intervals to 2050 for the TFR, the life expectancy for men and women, and net migration in 18 European countries. GARCH models are used for fertility and mortality, while net migration is modelled as an autoregressive process. Keywords: stochastic population forecast, empirical forecast errors, prediction intervals, GARCH-models, TFR, life expectancy, net migration, EEA. JEL classification: C22, J11.

• Keilman, Nico & Kinsella, Kevin (2004). Families and households in the 2000 round of censuses in ECE member countries.
• Keilman, Nico & Vogt, Yngve (2004). Utrygg pensjonsreform: Samfunnet kan tape skyhøye beløp. Aftenposten (morgenutg. : trykt utg.).  ISSN 0804-3116. Vis sammendrag

  Datasimuleringer viser at pensjonskommisjonen og politikerne er naive. De har ikke tatt hensyn til usikkerhetsanalyser i befolkningsutviklingen under planleggingen av det nye pensjonssystemet. Det kan koste samfunnet dyrt.

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