Relevant bibliographies by topics / Mortality forecast

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Mortality forecast'

Author: Grafiati
Published: 4 June 2021
Last updated: 13 February 2022

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Journal articles on the topic "Mortality forecast"

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Shang, Han Lin, and Steven Haberman. "FORECASTING MULTIPLE FUNCTIONAL TIME SERIES IN A GROUP STRUCTURE: AN APPLICATION TO MORTALITY." ASTIN Bulletin 50, no. 2 (February 18, 2020): 357–79. http://dx.doi.org/10.1017/asb.2020.3.

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AbstractWhen modelling subnational mortality rates, we should consider three features: (1) how to incorporate any possible correlation among subpopulations to potentially improve forecast accuracy through multi-population joint modelling; (2) how to reconcile subnational mortality forecasts so that they aggregate adequately across various levels of a group structure; (3) among the forecast reconciliation methods, how to combine their forecasts to achieve improved forecast accuracy. To address these issues, we introduce an extension of grouped univariate functional time-series method. We first consider a multivariate functional time-series method to jointly forecast multiple related series. We then evaluate the impact and benefit of using forecast combinations among the forecast reconciliation methods. Using the Japanese regional age-specific mortality rates, we investigate 1–15-step-ahead point and interval forecast accuracies of our proposed extension and make recommendations.
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Šiškina, Natalja, and Jonas Šiaulys. "ARMA Models for Mortality Forecast." Lietuvos statistikos darbai 55, no. 1 (December 20, 2016): 31–44. http://dx.doi.org/10.15388/ljs.2016.13865.

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In the last several decades, many countries have been paying a lot of attention to mortality forecastingbecause of high longevity risk. The purpose of this paper is to analyze mortality characteristics of Baltic countries andmake predictions using ARMA models. Research shoved that mortality rate distribution is almost the same in Lithuania, Latvia and Estonia and all of them represent longevity trends. It means that men and women, children and adults have thesame mortality structure in all Baltic countries and live longer than before.
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Šimpach, Ondřej, and Marie Šimpachová Pechrová. "Implications of the SARS-Cov-2 Pandemic for Mortality Forecasting: Case Study for the Czech Republic and Spain." Engineering Proceedings 5, no. 1 (July 18, 2021): 58. http://dx.doi.org/10.3390/engproc2021005058.

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The current pandemic situation of SARS-Cov-2 is negatively influencing people worldwide, and leading to high mortality and excess mortality, due to more reasons than only the disease itself. Thus, forecasting of the mortality rates and consequent population projections would have been complicated since 2020. Paper models mortality in the Czech Republic and Spain and assesses the possible impact of the COVID-19 on the forecasts. We use a Lee–Carter model and apply it to data from 1981 to 2019 (forecast A) and 1981 to 2020 (forecast B). Our results show differences in forecasts up to 2030 by mean square difference. The highest is in ages above 50 for Spain, where it was observed that the COVID-19 pandemic affected the mortality rates in a way that they were higher, and decreased at a slower pace than they would without taking 2020 into account. In the Czech Republic (CR), the forecast does not seem to be affected yet, but it could be in the future when the number of deaths (not only due to COVID-19, but altogether) increases significantly. Nevertheless, we have to verify our preliminary results on real data as soon as they are available.
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Inada, Haruhiko, Qingfeng Li, Abdulgafoor Bachani, and Adnan A. Hyder. "Forecasting global road traffic injury mortality for 2030." Injury Prevention 26, no. 4 (August 8, 2019): 339–43. http://dx.doi.org/10.1136/injuryprev-2019-043336.

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ObjectiveTo forecast the number and rate of deaths from road traffic injuries (RTI) in the world in 2030.MethodsThis study was a secondary analysis of annual country-level data of RTI mortality rates for 1990–2017 in the Global Burden of Disease (GBD) 2017 Study, population projection for 2030, gross domestic product (GDP) per capita for 1990–2030 and average years of schooling among people aged 15 years+ for 1990–2030. We developed up to 6884 combinations of forecasting models for each subgroup stratified by country, sex and mode of transport using linear and squared year, GDP per capita and average years of schooling as potential predictors. We conducted a fixed-size, rolling window out-of-sample forecast to choose the best combination for each subgroup. In the validation, we used the data for 1990–2002, 1991–2003 and 1992–2004 (fit periods) to forecast mortality rates in 2015, 2016 and 2017 (test periods), respectively. We applied the selected combination of models to the data for 1990–2017 to forecast the mortality rate in 2030 for each subgroup. To forecast the number of deaths, we multiplied the forecasted mortality rates by the corresponding population projection.ResultsDuring the test periods, the selected combination of models produced the number of deaths that is higher than that estimated in the GBD Study by 5.1% collectively. Our model resulted in 1.225 million deaths and 14.3 deaths per 100 000 population in 2030, which were 1% and 12% less than those for 2017 in the GBD Study, respectively.ConclusionsThe world needs to accelerate its efforts towards achieving the Decade of Action for Road Safety goal and the Sustainable Development Goals target.
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Bozikas, Apostolos, and Georgios Pitselis. "Credible Regression Approaches to Forecast Mortality for Populations with Limited Data." Risks 7, no. 1 (February 26, 2019): 27. http://dx.doi.org/10.3390/risks7010027.

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In this paper, we propose a credible regression approach with random coefficients to model and forecast the mortality dynamics of a given population with limited data. Age-specific mortality rates are modelled and extrapolation methods are utilized to estimate future mortality rates. The results on Greek mortality data indicate that credibility regression contributed to more accurate forecasts than those produced from the Lee–Carter and Cairns–Blake–Dowd models. An application on pricing insurance-related products is also provided.
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Khan, Md Hasinur Rahaman, Sadia Afrin, and Mohammad Shahed Masud. "Mortality Forecasting Using Lee Carter Model Implemented to French Mortality Data." Dhaka University Journal of Science 64, no. 2 (July 31, 2016): 99–104. http://dx.doi.org/10.3329/dujs.v64i2.54488.

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For the reason of simplicity the Lee and Carter (LC) method is getting widely adopted for long-run forecasts of age specific mortality rates. In this paper the LC model is applied to French mortality data to demonstrate the mortality results. The age-specific death rates are used for the period 1816 to 2006. The index of the level of mortality, and the shape and sensitivity coefficients for each age are obtained through the LC method. The autoregressive moving average and the singular value decomposition models are used to forecast the general index for a long period of time that goes from 2007 to 2056. The projection is useful since the projected mortality rates can be used to project life expectancy at birth which is the widely used social indicator in demography. Dhaka Univ. J. Sci. 64(2): 99-104, 2016 (July)
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Hainaut, Donatien. "A NEURAL-NETWORK ANALYZER FOR MORTALITY FORECAST." ASTIN Bulletin 48, no. 02 (January 9, 2018): 481–508. http://dx.doi.org/10.1017/asb.2017.45.

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AbstractThis article proposes a neural-network approach to predict and simulate human mortality rates. This semi-parametric model is capable to detect and duplicate non-linearities observed in the evolution of log-forces of mortality. The method proceeds in two steps. During the first stage, a neural-network-based generalization of the principal component analysis summarizes the information carried by the surface of log-mortality rates in a small number of latent factors. In the second step, these latent factors are forecast with an econometric model. The term structure of log-forces of mortality is next reconstructed by an inverse transformation. The neural analyzer is adjusted to French, UK and US mortality rates, over the period 1946–2000 and validated with data from 2001 to 2014. Numerical experiments reveal that the neural approach has an excellent predictive power, compared to the Lee–Carter model with and without cohort effects.
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Baran, S., J. Gáll, M. Ispány, and G. Pap. "Forecasting Hungarian mortality rates using the Lee-Carter method." Acta Oeconomica 57, no. 1 (March 1, 2007): 21–34. http://dx.doi.org/10.1556/aoecon.57.2007.1.3.

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A modified version of the popular Lee-Carter method (Lee-Carter 1992) is applied to forecast mortality rates in Hungary for the period 2004–2040 on the basis of mortality data between 1949 and 2003 both for men and women. Another case is also considered based on a restricted data set corresponding to the period 1989–2003. The model fitted to the data of the period 1949–2003 forecasts increasing mortality rates for men between ages 45 and 55, pointing out that the Lee-Carter method is hardly applicable for countries where mortality rates exhibit trends as peculiar as in Hungary. However, models fitted to the data for the last 15 years both for men and women forecast decreasing trends similarly to the case of countries where the method was successfully applied. Hence one gets a better fit in this way, however, further concerns suggest that the Lee-Carter model, which is celebrated and widely used in actuarial practice, does not necessarily give sufficiently good prediction.
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Shaki, Yair Y. "Quasi-Simpson paradox in estimating the expected mortality rate from the SARS-CoV-2." Science Progress 104, no. 2 (April 2021): 003685042110096. http://dx.doi.org/10.1177/00368504211009673.

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On January 30, 2020, the World Health Organization (WHO) declared SARS-CoV-2 a global pandemic, based on a high infection rate and a high case fatality rate (CFR). The combination of these two points led WHO to forecast a high expected mortality rate of approximately 2% of the population. The phenomenon of Simpson’s paradox teaches us that we should be careful when we combine two variables together. Indeed, despite the high mortality rate in several places, this forecast seems to have collapsed. We believe one of the reasons for the erroneous forecasts is that combining the above points ignored a confounding variable – many of the virus carriers are asymptomatic and therefore not diagnosed.
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Mohd Nor, Siti Rohani, Fadhilah Yusof, and Siti Mariam Norrulashikin. "Coherent Mortality Model in A State-Space Approach." Sains Malaysiana 50, no. 4 (April 30, 2021): 1101–11. http://dx.doi.org/10.17576/jsm-2021-5004-20.

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Mortality improvements that have recently become apparent in most developing countries have significantly shaped queries on forecast divergent between populations in recent years. Therefore, to ensure a more coherent way of forecasting, previous researchers have proposed multi-population mortality model in the form of independent estimation procedures. However, similar to single-population mortality model, such independent approaches might lead to inaccurate prediction interval. As a result of this inaccurate mortality forecasts, the life expectancies and the life annuities that the mortality model aims to generate is underestimated. In this study, we propose another new extension of the multi-population mortality model in a joint estimation approach by recasting the model into a state-space framework. A combination of augmented Li-Lee and O’Hare-Li methods are employed, before we transform the proposed model into a state-space formulation. In addition, this study incorporates the quadratic age effect parameter to the proposed model to better capture the younger ages mortality. We apply the method to gender and age-specific data for Malaysia. The results show that our latter framework brings a significant contribution to the multi-population mortality model due to the incorporation of joint-estimate and quadratic age effect parameters into the model’s structure. Consequently, the proposed model improves the mortality forecast accuracy.
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Dissertations / Theses on the topic "Mortality forecast"

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Zhu, Nan. "Essays on Lifetime Uncertainty: Models, Applications, and Economic Implications." Digital Archive @ GSU, 2012. http://digitalarchive.gsu.edu/rmi_diss/30.

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My doctoral thesis “Essays on Lifetime Uncertainty: Models, Applications, and Economic Implications” addresses economic and mathematical aspects pertaining to uncertainties in human lifetimes. More precisely, I commence my research related to life insurance markets in a methodological direction by considering the question of how to forecast aggregate human mortality when risks in the resulting projections is important. I then rely on the developed method to study relevant applied actuarial problems. In a second strand of research, I consider the uncertainty in individual lifetimes and its influence on secondary life insurance market transactions. Longevity risk is becoming increasingly crucial to recognize, model, and monitor for life insurers, pension plans, annuity providers, as well as governments and individuals. One key aspect to managing this risk is correctly forecasting future mortality improvements, and this topic has attracted much attention from academics as well as from practitioners. However, in the existing literature, little attention has been paid to accurately modeling the uncertainties associated with the obtained forecasts, albeit having appropriate estimates for the risk in mortality projections, i.e. identifying the transiency of different random sources affecting the projections, is important for many applications. My first essay “Coherent Modeling of the Risk in Mortality Projections: A Semi-Parametric Approach” deals with stochastically forecasting mortality. In contrast to previous approaches, I present the first data-driven method that focuses attention on uncertainties in mortality projections rather than uncertainties in realized mortality rates. Specifically, I analyze time series of mortality forecasts generated from arbitrary but fixed forecasting methodologies and historic mortality data sets. Building on the financial literature on term structure modeling, I adopt a semi-parametric representation that encompasses all models with transitions parameterized by a Normal distributed random vector to identify and estimate suitable specifications. I find that one to two random factors appear sufficient to capture most of the variation within all of our data sets. Moreover, I observe similar systematic shapes for their volatility components, despite stemming from different forecasting methods and/or different mortality data sets. I further propose and estimate a model variant that guarantees a non-negative process of the spot force of mortality. Hence, the resulting forward mortality factor models present parsimonious and tractable alternatives to the popular methods in situations where the appraisal of risks within medium or long-term mortality projections plays a dominant role. Relying on a simple version of the derived forward mortality factor models, I take a closer look at their applications in the actuarial context in the second essay “Applications of Forward Mortality Factor Models in Life Insurance Practice. In the first application, I derive the Economic Capital for a stylized UK life insurance company offering traditional product lines. My numerical results illustrate that (systematic) mortality risk plays an important role for a life insurer's solvency. In the second application, I discuss the valuation of different common mortality-contingent embedded options within life insurance contracts. Specifically, I present a closed-form valuation formula for Guaranteed Annuity Options within traditional endowment policies, and I demonstrate how to derive the fair option fee for a Guaranteed Minimum Income Benefit within a Variable Annuity Contract based on Monte Carlo simulations. Overall my results exhibit the advantages of forward mortality factor models in terms of their simplicity and compatibility with classical life contingencies theory. The second major part of my doctoral thesis concerns the so-called life settlement market, i.e. the secondary market for life insurance policies. Evolving from so-called “viatical settlements” popular in the late 1980s that targeted severely ill life insurance policyholders, life settlements generally involve senior insureds with below average life expectancies. Within such a transaction, both the liability of future contingent premiums and the benefits of a life insurance contract are transferred from the policyholder to a life settlement company, which may further securitize a bundle of these contracts in the capital market. One interesting and puzzling observation is that although life settlements are advertised as a high-return investment with a low “Beta”, the actual market systematically underperformed relative to expectations. While the common explanation in the literature for this gap between anticipated and realized returns falls on the allegedly meager quality of the underlying life expectancy estimates, my third essay “Coherent Pricing of Life Settlements under Asymmetric Information” proposes a different viewpoint: The discrepancy may be explained by adverse selection. Specifically, by assuming information with respect to policyholders’ health states is asymmetric, my model shows that a discrepancy naturally arises in a competitive market when the decision to settle is taken into account for pricing the life settlement transaction, since the life settlement company needs to shift its pricing schedule in order to balance expected profits. I derive practically applicable pricing formulas that account for the policyholder’s decision to settle, and my numerical results reconfirm that---depending on the parameter choices---the impact of asymmetric information on pricing may be considerable. Hence, my results reveal a new angle on the financial analysis of life settlements due to asymmetric information. Hence, all in all, my thesis includes two distinct research strands that both analyze certain economic risks associated with the uncertainty of individuals’ lifetimes---the first at the aggregate level and the second at the individual level. My work contributes to the literature by providing both new insights about how to incorporate lifetime uncertainty into economic models, and new insights about what repercussions---that are in part rather unexpected---this risk factor may have.
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Ramos, Anthony Kojo. "Forecasting Mortality Rates using the Weighted Hyndman-Ullah Method." Thesis, Mälardalens högskola, Akademin för utbildning, kultur och kommunikation, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-54711.

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The performance of three methods of mortality modelling and forecasting are compared. These include the basic Lee–Carter and two functional demographic models; the basic Hyndman–Ullah and the weighted Hyndman–Ullah. Using age-specific data from the Human Mortality Database of two developed countries, France and the UK (England&Wales), these methods are compared; through within-sample forecasting for the years 1999-2018. The weighted Hyndman–Ullah method is adjudged superior among the three methods through a comparison of mean forecast errors and qualitative inspection per the dataset of the selected countries. The weighted HU method is then used to conduct a 32–year ahead forecast to the year 2050.
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Mellkvist, Lars. "Den andres bröd : Levnadsrisk utifrån Lee-Cartermodellen." Thesis, Uppsala University, Department of Economics, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-9227.

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Under det gångna århundradet ökade den förväntade livslängden avsevärt såväl i Sverige som i övriga världen. 1900-talets förbättrade livslängd drevs inledningsvis av en minskad barnadödlighet medan de senare årtiondena kännetecknades av minskad dödlighet i höga åldrar.

En åldrande befolkning innebär ökade krav på sjukvård, äldreomsorg och inte minst pensionssystem. Pålitliga prognoser för vår framtida livslängd behövs för att beräkna de resurser som nämnda verksamheter kommer att ta i anspråk och utgör förutsättningen för en rättvis prissättning av försäkringsprodukter med levnadsrisk.

Lee-Carter-modellen är en av vår tids tongivande modeller för mortalitetsprognostisering. Modellen används här för att göra livslängdsprognoser utifrån svenska mortalitetsdata; prognoserna jämförs sedan med observerade utfall.

Mot bakgrund av resultatet diskuteras levnadsrisk med fokus på pensioner.

Inte oväntat presterar prognoserna ingen felfri bild av verkligheten och prognosfelet varierar i storlek mellan skattningarna; att använda dem som underlag för pensionsberäkningar hade i förlängningen varit ohållbart. Exemplet illustrerar på samma gång vår osäkerhet inför framtidens livslängdsutveckling och svårigheten i att prognostisera den.


During the past century, Sweden along with many other countries experienced a sharp decline in mortality rates. The increased life expectancy was initially propelled by mortality reductions among infants and subsequently by a survival improvement in advanced ages.

An ageing population has large implications for those providing services to the elderly, such as medical care and pensions, whilst also addressing the need for accurate and reliable mortality forecasts and projection methods.

The Lee-Carter model is the current gold standard for mortality forecasting and has been widely adopted in several studies. Here, the model is applied on Swedish mortality data; the projections are then compared to the observed lifespan development. Against this backdrop, a discussion on longevity risk in pensions schemes follows.

The forecasts performed in this study do not perfectly reflect the observed mortality change in the examined period; furthermore, the variation of the estimation errors limits the actuarial value of the projections. The findings illuminate the uncertainty that surrounds our future life expectancy as well as the difficulties associated with forecasting it.

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Foreman, Kyle Jayson. "Trends and forecasts in cause-specific mortality." Thesis, Imperial College London, 2015. http://hdl.handle.net/10044/1/61344.

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Mortality forecasting models are typically limited in that they pertain only to national death rates, predict only all-cause mortality, or do not capture and utilize the correlation among diseases. I have developed a novel Bayesian hierarchical model that jointly forecasts cause- specific death rates for geographic subunits. I examined the model’s effectiveness by applying it to United States vital statistics data from 1982 to 2011 that I prepared using a new cause of death reassignment algorithm. I found that the model not only generated coherent forecasts for mutually exclusive causes of death, but it also exhibited lower out-of-sample error than alternative commonly-used models for forecasting mortality. I then used the model to produce forecasts of US cause-specific mortality through 2025 and analysed the resulting trends. I found that total death rates in the US were likely to continue their decline, but at a slower rate of improvement than has been observed for the past several decades. While death rates due to major causes of death like ischaemic heart disease, stroke, and lung cancer were projected to continue trending downward, increases in causes such as unintentional injuries and mental and neurological conditions offset many of these gains. These findings suggest that the US health system will need to adapt to a changing cause composition of disease burden as its population ages in the coming decade. Forecasting research should continue to consider how to best incorporate and balance the many dimensions of mortality when producing projections.
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Moura, Maria Valdiva Barbosa. "CenÃrios de PrevisÃo para a rotatividade das MPEs no mercado: um estudo de caso." Universidade Federal do CearÃ, 2008. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=2358.

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Universidade Federal do CearÃ
Este estudo pretende apresentar o perfil do empreendedor e quantificar os fatores determinantes da mortalidade das micro e pequenas empresas (MPEs), cuja taxa mÃdia anual brasileira à acima de 50%. AlÃm desse foco a pesquisa procura resgatar o histÃrico das aÃÃes empreendedoras e das polÃticas de incentivo ao empreendedorismo de pequeno porte, no Brasil e em outros paÃses. Uma vez que esse segmento à de real importÃncia para o paÃs, haja vista contribuir sobremaneira para geraÃÃo de emprego e renda com participaÃÃo de 20% no PIB nacional, e consequentemente na reduÃÃo da desigualdade social, um trabalho que se propÃe a analisar as causas da elevada taxa de mortalidade se identifica como relevante. Para atingir o objetivo foi desenvolvida uma pesquisa de campo, atravÃs de uma amostra representativa junto aos sÃcios principais ou proprietÃrios de empresas ativas e extintas constituÃdas no perÃodo de 2001 a 2005 na cidade de Picos/PI. Foram coletadas informaÃÃes sobre as caracterÃsticas das empresas como tambÃm sobre os perfis dos sÃcios e, uma vez tabulados foram confrontados com os dados consolidados de uma pesquisa do SEBRAE/2005 para a cidade de SÃo Paulo, as quais mostraram conformidade, exceto quanto a variÃvel nÃvel de escolaridade. AtravÃs de um modelo de escolha binÃria foram construÃdos vÃrios cenÃrios de previsÃo, variando de situaÃÃes extremamente otimistas Ãs pessimistas quanto Ãs caracterÃsticas das empresas e perfis dos sÃcios, de onde foram estimadas probabilidades para uma empresa representativa vir a ser extinta. A justeza do modelo se deu de forma estatisticamente satisfatÃria, vez que as previsÃes dos cenÃrios pessimistas e otimistas revelaram probabilidades de 99% e 1%, respectivamente, para ocorrÃncia de insucesso de uma empresa implantada.
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Eendebak, Robert. "The potential relationships between hormone biomarkers and functional and health outcomes of ageing." Thesis, University of Manchester, 2017. https://www.research.manchester.ac.uk/portal/en/theses/the-potential-relationships-between-hormone-biomarkers-and-functional-and-health-outcomes-of-ageing(e28321cc-703c-44df-99b4-fb0d76f7f429).html.

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Although the female menopause has been extensively characterized as a well-defined symptomatic state of oestrogen deficiency, which responds relatively well to oestrogen replacement therapy, the symptomatic state of androgen deficiency in men is poorly defined and uncertainty exists whether it responds to testosterone replacement. It has been proposed that hypothalamic-pituitary-testicular (HPT)-axis function (responsible for the production of androgens) and regulation could be viewed as a ‘barometer’ of health status in older men and that potential alterations in HPT-axis function and regulation reflect subclinical and clinical deficits in function and health, which may result in an aged phenotype of human health and disease in older men. The HPT-axis constitutes a well-defined, tractable, clinically-relevant, biological system, which may permit insight into the mechanisms underlying the expression of ageing-related phenotypes of human health and disease. By using a different lens – such as the genetic background; the compensatory responses within the HPT-axis; the syndromes of androgen deficiency; the ethnic background of an individual or the life course trajectory of function and health from conception into older age – to magnify potential dysregulation in the HPT-axis will it be possible to visualize and understand the phenotypic expression of human male ageing as a gradient of functional and health outcomes. This will allow for a better understanding of the physiological mechanics underlying symptomatic expression of dysregulation in the HPT-axis.
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Hu, Zhi-Jiang, and 胡志強. "Model Analysis and Forecast Evalution for the Mortality of Cause of Death in Taiwan." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/45740378362560403301.

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碩士
真理大學
統計與精算學系碩士班
103
Mortality prediction is very important issue. Mortality is often miscalculated in the commodity of life insurance so that policyholders or insurance company pay more premiums or received less premiums. Therefore, the estimated mortality often attract the attention form many experts and industries. In the past the mortality model use Lee-Carter model to predict changes by assuming that mortality of cause of death between variances are independent. 　　Therefore, in this study we use the vector error correction model (VECM) to fit the mortality of cause of death model, and use the autoregressive integrated moving average model (ARIMA) and the vector autoregressive model (VAR) model to make the efficiency assessment of prediction.
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Tang, Kim Oanh. "Projection de la mortalité aux âges avancées au Canada : comparaison de trois modèles." Thèse, 2009. http://hdl.handle.net/1866/7899.

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Papřoková, Petra. "Prognóza vývoje obyvatelstva Moravskoslezského kraje do roku 2050." Master's thesis, 2017. http://www.nusl.cz/ntk/nusl-365470.

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Moravskoslezský region population forecast to 2050 Abstract The main goal of this thesis is to calculate Moravskoslezský region population forecast to 2050 and show how future population growth will likely be in the next decades. It includes a detailed population development analysis which is an important part of this thesis. Moravskoslezský region is still one of the most populous regions of Czechia, despite of natural decrease and negative net migration in past 21 years (except 2007 and 2008). Based on the analysis, we can assume that population decline will continue in the future and population forecast results confirm this hypothesis. Declining population size will be followed by significant age-structure changes in population and progressive demographic ageing. From the perspective of future population development Moravskoslezský region will face brand new situations and related problems. Keywords: population forecast, Moravskoslezský region, analysis of population development, age structure, fertility, mortality, migration, regional development, cohort-component method
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Li, Hua, and 黎華. "Forecasts of Taiwan R.O.C. Mortality, Fertility, Migration and Population from 2014 to 2034 Using a Bayesian Approach." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/66060185799986152614.

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碩士
國立陽明大學
公共衛生研究所
102
Population is one of the most important factors for developments of a country. Forecasts of future population is thus a key step for policy planning on economic growth, education, health and medical care, social welfare system, etc. that ensure sustainable developments. Conventional practices of population forecasts are based on deterministic procedures, which fail to account for various uncertainties in forecasting. In this thesis, I study Taiwan population forecasts during 2014-2034, using instead a Bayesian approach, so that all known uncertainties in forecasting are taken into account. I consider the use of one–component space models, or the Lee-Carter type models, for the multivariate time series of the sex- and age-specific mortality rates and net migration counts, and the use of a two-component state space models, which addresses both the linear declining trend and the recent turnover, for women age-specific fertility rates in Taiwan. With historical mortality, fertility, and migration data in Taiwan since 1975, I apply the Markov-chain Monte Carlo together with the Kalman filter method to obtain the Bayesian posterior distributions for the model parameters and the Bayesian predictive distributions for the mortality rates, fertility rates, and net migration counts. Based on the predictive distributions of these demographic quantities as well as the well-known cohort component method, I then obtain sex- and age-specific population forecasts and their Bayesian prediction intervals. The resulting population forecasts also provide forecasts and prediction intervals for widely-used demographic indices such as the total fertility, aging index, and dependence ratio, which are useful for examining future characteristics and profiles of Taiwan population.
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Books on the topic "Mortality forecast"

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Skiadas, Christos H. The health state function of a population: Stochastic modeling and the first exit time theory in demography, derivation and classification of human development age groups, healthy life expectancy estimates, fitting curves and forecasts. Athens, Greece: National Library of Greece, 2013.

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Moreno, Rui. Organ failure scoring. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199600830.003.0030.

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The general outcome prediction models were not, by design, developed to track individual patients. They provided an indication of death risks for groups of ICU patients. Hence, investigators created organ failure scores. Instruments, such as Sequential Organ Failure Assessment (SOFA), Multiple Organ Dysfunction Score, or Logistic Organ Dysfunction Score are designed to evaluate separately the six most important organ systems in critically-ill patients sequentially, taken on a daily basis. Easy to perform, designed to be done at bedside, they do not forecast ICU or hospital mortality (apart from the SOFA score), but are very useful in describing the patient and his response to therapy.
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Book chapters on the topic "Mortality forecast"

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Keilman, Nico, and Sigve Kristoffersen. "European Mortality Forecasts: Are the Targets Still Moving?" In Developments in Demographic Forecasting, 179–92. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-42472-5_9.

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Abstract Many statistical agencies routinely produce population forecasts, and revise these forecasts when new data become available, or when current demographic trends indicate that an update is necessary. When the forecaster strongly revises, from one forecast round to the next one, a forecast for a certain target year (for instance the life expectancy in 2050), this indicates large uncertainty connected to mortality predictions. The aim of this chapter is to shed more light on the uncertainty in mortality forecasts, by analysing the extent to which life expectancy predictions for 2030 and 2050 were revised in subsequent rounds of population forecasts published by statistical agencies in selected countries. It updates and extends earlier work that focused on United Nations and Eurostat forecasts published between 1994 and 2004 (Keilman et al. 2008). There the conclusion was that life expectancy forecasts for 18 European countries for the year 2050 had been revised upwards systematically, by around 2 years on average during the 10-year publication period. A recent analysis based on official population forecasts for Norway published in the period 1999–2018 led to the same conclusion (Keilman 2018). Here we will show that the period of upward revisions seems to have ended for some European countries.
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Bergeron-Boucher, Marie-Pier, Søren Kjæ rgaard, Marius D. Pascariu, José Manuel Aburto, Jesús-Adrián Alvarez, Ugofilippo Basellini, Silvia Rizzi, and James W. Vaupel. "Alternative Forecasts of Danish Life Expectancy." In Developments in Demographic Forecasting, 131–51. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-42472-5_7.

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Abstract In the last three decades, considerable progress in mortality forecasting has been achieved, with new and more sophisticated models being introduced. Most of these forecasting models are based on the extrapolation of past trends, often assuming linear (or log-linear) development of mortality indicators, such as death rates or life expectancy. However, this assumption can be problematic in countries where mortality development has not been linear, such as in Denmark. Life expectancy in Denmark experienced stagnation from the 1980s until the mid-1990s. To avoid including the effect of the stagnation, Denmark’s official forecasts are based on data from 1990 only. This chapter is divided into three parts. First, we highlight and discuss some of the key methodological issues for mortality forecasting in Denmark. How many years of data are needed to forecast? Should linear extrapolation be used? Second, we compare the forecast performance of 11 models for Danish females and males and for period and cohort data. Finally, we assess the implications of the various forecasts for Danish society, and, in particular, their implications for future lifespan variability and age at retirement.
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Basellini, Ugofilippo, and Carlo Giovanni Camarda. "A Three-Component Approach to Model and Forecast Age-at-Death Distributions." In Developments in Demographic Forecasting, 105–29. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-42472-5_6.

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Abstract Mortality forecasting has recently received growing interest, as accurate projections of future lifespans are needed to ensure the solvency of insurance and pension providers. Several innovative stochastic methodologies have been proposed in most recent decades, the majority of them being based on age-specific mortality rates or on summary measures of the life table. The age-at-death distribution is an informative life-table function that provides readily available information on the mortality pattern of a population, yet it has been mostly overlooked for mortality projections. In this chapter, we propose to analyse and forecast mortality developments over age and time by introducing a novel methodology based on age-at-death distributions. Our approach starts from a nonparametric decomposition of the mortality pattern into three independent components corresponding to Childhood, Early-Adulthood and Senescence, respectively. We then model the evolution of each component-specific death density with a relational model that associates a time-invariant standard to a series of observed distributions by means of a transformation of the age axis. Our approach allows us to capture mortality developments over age and time, and forecasts can be derived from parameters’ extrapolation using standard time series models. We illustrate our methods by estimating and forecasting the mortality pattern of females and males in two high-longevity countries using data of the Human Mortality Database. We compare the forecast accuracy of our model and its projections until 2050 with three other forecasting methodologies.
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Graziani, Rebecca. "Stochastic Population Forecasting: A Bayesian Approach Based on Evaluation by Experts." In Developments in Demographic Forecasting, 21–42. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-42472-5_2.

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Abstract We suggest a procedure for deriving expert based stochastic population forecasts within the Bayesian approach. According to the traditional and commonly used cohort-component model, the inputs of the forecasting procedures are the fertility and mortality age schedules along with the distribution of migrants by age. Age schedules and distributions are derived from summary indicators, such as total fertility rates, male and female life expectancy at birth, and male and female number of immigrants and emigrants. The joint distributions of all summary indicators are obtained based on evaluations by experts, elicited according to a conditional procedure that makes it possible to derive information on the centres of the indicators, their variability, their across-time correlations, and the correlations between the indicators. The forecasting method is based on a mixture model within the Supra-Bayesian approach that treats the evaluations by experts as data and the summary indicators as parameters. The derived posterior distributions are used as forecast distributions of the summary indicators of interest. A Markov Chain Monte Carlo algorithm is designed to approximate such posterior distributions.
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Booth, Heather. "Coherent Mortality Forecasting with Standards: Low Mortality Serves as a Guide." In Developments in Demographic Forecasting, 153–78. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-42472-5_8.

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Abstract Mortality forecasts are an important component of population forecasting and are central to the estimation of longevity risk in actuarial practice. Planning by the state for health and aged care services and by individuals for retirement and later life depends on accurate mortality forecasts. The overall accuracy or performance of mortality forecasting has improved since Lee and Carter (1992) introduced stochastic forecasting of mortality to the demographic community, and further improvements can undoubtedly be made.
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Lee, Ronald. "Mortality Forecasts and Linear Life Expectancy Trends." In Demographic Research Monographs, 167–83. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05075-7_14.

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Tuljapurkar, Shripad. "Stochastic Forecasts of Mortality, Population and Pension Systems." In Demographic Research Monographs, 145–55. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05075-7_12.

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Cruijsen, Harri, and Harold Eding. "The Latest Mortality Forecasts in the European Union." In European Studies of Population, 227–58. Dordrecht: Springer Netherlands, 2001. http://dx.doi.org/10.1007/0-306-47562-6_10.

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Van Hoorn, Wim, and Joop De Beer. "Projecting Mortality in Population Forecasts in the Netherlands." In European Studies of Population, 205–26. Dordrecht: Springer Netherlands, 2001. http://dx.doi.org/10.1007/0-306-47562-6_9.

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Knowlton, Kim, Christian Hogrefe, Barry Lynn, Cynthia Rosenzweig, Joyce Rosenthal, and Patrick L. Kinney. "Impacts of Heat and Ozone on Mortality Risk in the New York City Metropolitan Region Under a Changing Climate." In Seasonal Forecasts, Climatic Change and Human Health, 143–60. Dordrecht: Springer Netherlands, 2008. http://dx.doi.org/10.1007/978-1-4020-6877-5_9.

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Conference papers on the topic "Mortality forecast"

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Safitri, Y. R., S. Mardiyati, and M. Malik. "The Cairns-Blake-Dowd model to forecast Indonesian mortality rates." In PROCEEDINGS OF THE 4TH INTERNATIONAL SYMPOSIUM ON CURRENT PROGRESS IN MATHEMATICS AND SCIENCES (ISCPMS2018). AIP Publishing, 2019. http://dx.doi.org/10.1063/1.5132466.

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Reports on the topic "Mortality forecast"

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Andreev, Kirill F., and James W. Vaupel. Forecasts of cohort mortality after age 50. Rostock: Max Planck Institute for Demographic Research, May 2006. http://dx.doi.org/10.4054/mpidr-wp-2006-012.

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