The Experts below are selected from a list of 38835 Experts worldwide ranked by ideXlab platform

Adrian E. Raftery - One of the best experts on this subject based on the ideXlab platform.

  • accounting for uncertainty about past values in probabilistic Projections of the total fertility rate for most countries
    The Annals of Applied Statistics, 2020
    Co-Authors: Peiran Liu, Adrian E. Raftery
    Abstract:

    Since the 1940s, Population Projections have in most cases been produced using the deterministic cohort component method. However, in 2015, for the first time and in a major advance, the United Nations issued official probabilistic Population Projections for all countries based on Bayesian hierarchical models for total fertility and life expectancy. The estimates of these models and the resulting Projections are conditional on the U.N.’s official estimates of past values. However, these past values are themselves uncertain, particularly for the majority of the world’s countries that do not have longstanding high-quality vital registration systems, when they rely on surveys and censuses with their own biases and measurement errors. This paper extends the U.N. model for projecting future total fertility rates to take account of uncertainty about past values. This is done by adding an additional level to the hierarchical model to represent the multiple data sources, in each case estimating their bias and measurement error variance. We assess the method by out-of-sample predictive validation. While the prediction intervals produced by the extant method (which does not account for this source of uncertainty) have somewhat less than nominal coverage, we find that our proposed method achieves closer to nominal coverage. The prediction intervals become wider for countries for which the estimates of past total fertility rates rely heavily on surveys rather than on vital registration data, especially in high fertility countries.

  • Accounting for Uncertainty About Past Values In Probabilistic Projections of the Total Fertility Rate for All Countries.
    arXiv: Applications, 2018
    Co-Authors: Peiran Liu, Adrian E. Raftery
    Abstract:

    Since the 1940s, Population Projections have in most cases been produced using the deterministic cohort component method. However, in 2015, for the first time, in a major advance, the United Nations issued official probabilistic Population Projections for all countries based on Bayesian hierarchical models for total fertility and life expectancy. The estimates of these models and the resulting Projections are conditional on the UN's official estimates of past values. However, these past values are themselves uncertain, particularly for the majority of the world's countries that do not have longstanding high-quality vital registration systems, when they rely on surveys and censuses with their own biases and measurement errors. This paper is a first attempt to remedy this for total fertility rates, by extending the UN model for the future to take account of uncertainty about past values. This is done by adding an additional level to the hierarchical model to represent the multiple data sources, in each case estimating their bias and measurement error variance. We assess the method by out-of-sample predictive validation. While the prediction intervals produced by the current method have somewhat less than nominal coverage, we find that our proposed method achieves close to nominal coverage. The prediction intervals become wider for countries for which the estimates of past total fertility rates rely heavily on surveys rather than on vital registration data.

  • probabilistic Population Projections for countries with generalized hiv aids epidemics
    Population Studies-a Journal of Demography, 2018
    Co-Authors: David J Sharrow, Jessica Godwin, Samuel J Clark, Adrian E. Raftery
    Abstract:

    In 2015, the United Nations (UN) issued probabilistic Population Projections for all countries up to 2100, by simulating future levels of total fertility and life expectancy and combining the results using a standard cohort component projection method. For the 40 countries with generalized HIV/AIDS epidemics, the mortality Projections used the Spectrum/Estimation and Projection Package (EPP) model, a complex, multistate model designed for short-term Projections of policy-relevant quantities for the epidemic. We propose a simpler approach that is more compatible with existing UN projection methods for other countries. Changes in life expectancy are projected probabilistically using a simple time series regression and then converted to age- and sex-specific mortality rates using model life tables designed for countries with HIV/AIDS epidemics. These are then input to the cohort component method, as for other countries. The method performed well in an out-of-sample cross-validation experiment. It gives similar short-run Projections to Spectrum/EPP, while being simpler and avoiding multistate modelling.

  • bayesPop: Probabilistic Population Projections
    Journal of statistical software, 2016
    Co-Authors: Hana Ševčíková, Adrian E. Raftery
    Abstract:

    We describe bayesPop, an R package for producing probabilistic Population Projections for all countries. This uses probabilistic Projections of total fertility and life expectancy generated by Bayesian hierarchical models. It produces a sample from the joint posterior predictive distribution of future age- and sex-specific Population counts, fertility rates and mortality rates, as well as future numbers of births and deaths. It provides graphical ways of summarizing this information, including trajectory plots and various kinds of probabilistic Population pyramids. An expression language is introduced which allows the user to produce the predictive distribution of a wide variety of derived Population quantities, such as the median age or the old age dependency ratio. The package produces aggregated Projections for sets of countries, such as UN regions or trading blocs. The methodology has been used by the United Nations to produce their most recent official Population Projections for all countries, published in the World Population Prospects.

  • probabilistic Population Projections for countries with generalized hiv aids epidemics
    arXiv: Applications, 2016
    Co-Authors: David J Sharrow, Jessica Godwin, Samuel J Clark, Adrian E. Raftery
    Abstract:

    The United Nations (UN) issued official probabilistic Population Projections for all countries to 2100 in July 2015. This was done by simulating future levels of total fertility and life expectancy from Bayesian hierarchical models, and combining the results using a standard cohort-component projection method. The 40 countries with generalized HIV/AIDS epidemics were treated differently from others, in that the Projections used the highly multistate Spectrum/EPP model, a complex 15-compartment model that was designed for short-term Projections of quantities relevant to policy for the epidemic. Here we propose a simpler approach that is more compatible with the existing UN probabilistic projection methodology for other countries. Changes in life expectancy are projected probabilistically using a simple time series regression model on current life expectancy, HIV prevalence and ART coverage. These are then converted to age- and sex-specific mortality rates using a new family of model life tables designed for countries with HIV/AIDS epidemics that reproduces the characteristic hump in middle adult mortality. These are then input to the standard cohort-component method, as for other countries. The method performed well in an out-of-sample cross-validation experiment. It gives similar Population Projections to Spectrum/EPP in the short run, while being simpler and avoiding multistate modeling.

Roberto Roson - One of the best experts on this subject based on the ideXlab platform.

  • g rdem a gtap based recursive dynamic cge model for long term baseline generation and analysis
    Social Science Research Network, 2018
    Co-Authors: Wolfgang Britz, Roberto Roson
    Abstract:

    We motivate and detail the newly developed G-RDEM recursive-dynamic Computable General Equilibrium model as a tool for long-term counterfactual analysis and baseline generation from given GDP and Population Projections. It encompasses an AIDADS demand system with non-linear Engel curves, debt accumulation from foreign saving and introduces sector specific productivity changes, endogenous aggregate saving rates, as well as time-varying input-output coefficients. Parameters for these relationships are econometrically estimated or taken from published work. The core of the model is derived from the GTAP standard model and seamlessly incorporated into the modular and flexible CGEBox modelling platform. Accordingly, it can be applied with various other extensions such as GTAP-AEZ, GTAP-Water or a regional breakdown for Europe to 280 NUTS2 regions. G-RDEM maintains the flexible aggregation from the GTAP data base. It is open source, encoded in GAMS and can be steered by a Graphical User Interface, which also encompasses a tool to analyse results with tables, graphs and maps. Existing GDP and Population Projections for the Socio-Economic Pathways 1-5 can be directly incorporated for baseline construction. A comparison of the generated long-term structural composition of the economy against a simple recursive-dynamic variant, using the basic CDE demand system of the standard GTAP model, uniform productivity growth, fixed saving rates and technology parameters, and no debt accumulation shows that G-RDEM brings about much more plausible results, as well as a more realistic, internally consistent representation of the economic structure in a hypothetical future.

Wolfgang Lutz - One of the best experts on this subject based on the ideXlab platform.

  • forecasting societies adaptive capacities through a demographic metabolism model
    Nature Climate Change, 2017
    Co-Authors: Wolfgang Lutz, Raya Muttarak
    Abstract:

    In seeking to understand how future societies will be affected by climate change we cannot simply assume they will be identical to those of today, because climate and societies are both dynamic. Here we propose that the concept of demographic metabolism and the associated methods of multi-dimensional Population Projections provide an effective analytical toolbox to forecast important aspects of societal change that affect adaptive capacity. We present an example of how the changing educational composition of future Populations can influence societies' adaptive capacity. Multi-dimensional Population Projections form the human core of the Shared Socioeconomic Pathways scenarios, and knowledge and analytical tools from demography have great value in assessing the likely implications of climate change on future human well-being.

  • dimensions of global Population Projections what do we know about future Population trends and structures
    Philosophical Transactions of the Royal Society B, 2010
    Co-Authors: Wolfgang Lutz, K C Samir
    Abstract:

    The total size of the world Population is likely to increase from its current 7 billion to 8–10 billion by 2050. This uncertainty is because of unknown future fertility and mortality trends in different parts of the world. But the young age structure of the Population and the fact that in much of Africa and Western Asia, fertility is still very high makes an increase by at least one more billion almost certain. Virtually, all the increase will happen in the developing world. For the second half of the century, Population stabilization and the onset of a decline are likely. In addition to the future size of the Population, its distribution by age, sex, level of educational attainment and place of residence are of specific importance for studying future food security. The paper provides a detailed discussion of different relevant dimensions in Population Projections and an evaluation of the methods and assumptions used in current global Population Projections and in particular those produced by the United Nations and by IIASA.

  • Probabilistic Population Projections for India with explicit consideration of the education-fertility link
    International Statistical Review, 2007
    Co-Authors: Wolfgang Lutz, Sergei Scherbov
    Abstract:

    Summary Among the different sources of uncertainty in Population forecasting, uncertain changes in the structure of heterogeneous Populations have received little attention so far, although they can have significant impacts. Here we focus on the effect of changes in the educational composition of the Population on the overall fertility of the Population in the presence of strong fertility differentials by education. With data from India we show that alternative paths of future female enrolment in education result in significantly different total fertility rates (TFR) for the country over the coming decades, even assuming identical fertility trends within each education group. These results from multi-state Population Projections by education are then translated into a fully probabilistic Population projection for India in which the results of alternative education scenarios are assumed to expand the uncertainty range of the future TFR in the total Population. This first attempt to endogenize structural change with respect to education—which is the greatest measurable source of fertility heterogeneity in Asia—has resulted from a larger exercise of the Asian MetaCentre for Population and Sustainable Development Analysis to collect empirical information, scientific arguments as well as informed opinions about likely future Population trends in Asia from a large number of Population experts in the region. In this process, future changes in the educational composition of the Population have been identified as a key driver of future fertility. The actual probabilistic Population Projections for India show that with high certainty, the Indian Population will continue to grow to about 1.3 billion over the next quarter of a century. After that the uncertainty will get much wider, ranging from a continued strong increase to the beginning of a Population decline in India. Resume Parmi les differentes sources d'incertitude dans les Projections de Population, les changements dans la structure de Populations heterogenes ont recu peu d'attention jusqu'ici, en depit de l'impact significatif qu'ils peuvent avoir. Nous nous concentrons ici sur les effets de changements dans la composition educative de la Population sur la fertilite globale de la Population, en presence de forts differentiels de fertilite par niveau d'education. A partir de donnees de l'Inde, nous montrons que des parcours educatifs differents des femmes conduisent a des taux globaux de fertilite significativement differents pour le pays dans les decennies a venir, meme avec l'hypothese que les tendances de fertilite restent identiques a l'interieur de chaque groupe educatif. Ces resultats de Projections de Population par niveau educatif sont ensuite traduits en une projection de Population entierement probabiliste pour l'Inde, dans laquelle les resultats de scenarios educatifs alternatifs sont supposes accroitre le degre d'incertitude du futur taux global de fertilite dans la Population totale. Cette premiere tentative d'endogeneiser le changement structurel en fonction de l'education—qui est la source la plus mesurable d'heterogeneite de la fertilite en Asie—est issue d'un exercice plus large conduit par le Metacentre Asiatique pour l'analyse de la Population et du developpement durable dans le but de collecter de l'information empirique, des arguments scientifiques ainsi que l'opinion d'un grand nombre d'experts de la Population de la region sur les futures tendances probables de la Population en Asie. Dans ce processus, les changements futurs dans la structure de la Population par niveau d'education ont ete identifies comme un facteur cle de la fertilite future. Les Projections de Population probabilistes pour l'Inde montrent avec un degreeleve de certitude que la Population va continuer a croitre jusqu'a environ 1.3 milliard dans le prochain quart de siecle. Au dela l'incertitude devient beaucoup plus grande, entre le prolongement d'un fort accroissement et le ebut d'une baisse de la Population indienne.

  • an expert based framework for probabilistic national Population Projections the example of austria
    European Journal of Population-revue Europeenne De Demographie, 1998
    Co-Authors: Wolfgang Lutz, Sergei Scherbov
    Abstract:

    The traditional way of dealing with uncertainty in Population Projections through high and low variants is unsatisfactory because it remains unclear what range of uncertainty these alternative paths are assumed to cover. But probabilistic approaches have not yet found their way into official Population Projections. This paper proposes an expert-based probabilistic approach that seems to meet important criteria for successful application to national and international Projections: 1) it provides significant advantages to current practice, 2) it presents an evolution of current practice rather than a discontinuity, 3) it is scientifically sound, and 4) it is applicable to all countries.

  • expert based probabilistic Population Projections
    Population and Development Review, 1998
    Co-Authors: Wolfgang Lutz, Warren C Sanderson, Sergei Scherbov
    Abstract:

    Most users of Population Projections are interested in one likely path of future Population trends based on the best existing knowledge. Whether it is called the medium variant, central scenario, or median of an uncertainty distribution, this projected path will be taken as a forecast on which further considerations can be based. For many users such a best guess will suffice. It can be taken as an exogenous input into their own models for school planning, social security considerations, energy outlook, and the like. For this reason a medium projection is an indispensable component of any set of published Projections intended for practical use.

Patrick Gerland - One of the best experts on this subject based on the ideXlab platform.

  • patterns of fertility decline and the impact of alternative scenarios of future fertility change in sub saharan africa
    Population and Development Review, 2017
    Co-Authors: Patrick Gerland, Ann E Biddlecom, Vladimira Kantorova
    Abstract:

    Our aim in this chapter is to provide an updated and concise description of the diversity of fertility decline patterns among countries1 in sub- Saharan Africa drawing on the latest series of fertility estimates that take into account many different data sources and that are harmonized with other demographic components (United Nations 2015d). We focus on the level of fertility prior to the start of fertility decline the time period of the fertility transition and the estimated pace of decline. We also explore the implications of different fertility decline patterns for future fertility and Population Projections in the region. We draw on the distinct patterns of fertility decline among countries worldwide that are advanced in (or have completed) their first fertility transition to construct probabilistic fertility and Population Projections for sub-Saharan African countries. The illustrative comparisons of Projections highlight the demographic impact if future fertility decline in sub-Saharan countries were to accelerate and follow the rapid pace of decline already experienced by a diverse group of countries. (excerpt)

  • age specific mortality and fertility rates for probabilistic Population Projections
    arXiv: Applications, 2016
    Co-Authors: Hana Ševčíková, Patrick Gerland, Vladimira Kantorova, Adrian E. Raftery
    Abstract:

    The UN released official probabilistic Population Projections (PPP) for all countries for the first time in July 2014. These were obtained by projecting the period total fertility rate (TFR) and life expectancy at birth (e0) using Bayesian hierarchical models, yielding a large set of future trajectories of TFR and e0 for all countries and future time periods to 2100, sampled from their joint predictive distribution. Each trajectory was then converted to age-specific mortality and fertility rates, and Population was projected using the cohort-component method. This yielded a large set of trajectories of future age- and sex-specific Population counts and vital rates for all countries. In this chapter we describe the methodology used for deriving the age-specific mortality and fertility rates in the 2014 PPP, we identify limitations of these methods, and we propose several methodological improvements to overcome them. The methods presented in this chapter are implemented in the publicly available bayesPop R package.

  • the united nations probabilistic Population Projections an introduction to demographic forecasting with uncertainty
    Foresight: The International Journal of Applied Forecasting, 2015
    Co-Authors: Leontine Alkema, Patrick Gerland, Adrian E. Raftery, John R Wilmoth
    Abstract:

    The United Nations publishes Projections of Populations around the world and breaks these down by age and sex. Traditionally, they are produced with standard demographic methods based on assumptions about future fertility rates, survival probabilities, and migration counts. Such Projections, however, were not accompanied by formal statements of uncertainty expressed in probabilistic terms. In July 2014 the UN for the first time issued official probabilistic Population Projections for all countries to 2100. These Projections quantify uncertainty associated with future fertility and mortality trends worldwide. This review article summarizes the probabilistic Population projection methods and presents forecasts for Population growth over the rest of this century.

  • world Population stabilization unlikely this century
    Science, 2014
    Co-Authors: Patrick Gerland, Adrian E. Raftery, Hana Ševčíková, Leontine Alkema, Thomas Spoorenberg, Bailey K Fosdick, Jennifer Chunn, Nevena Lalic, Guiomar Bay, Thomas Buettner
    Abstract:

    The United Nations (UN) recently released Population Projections based on data until 2012 and a Bayesian probabilistic methodology. Analysis of these data reveals that, contrary to previous literature, the world Population is unlikely to stop growing this century. There is an 80% probability that world Population, now 7.2 billion people, will increase to between 9.6 billion and 12.3 billion in 2100. This uncertainty is much smaller than the range from the traditional UN high and low variants. Much of the increase is expected to happen in Africa, in part due to higher fertility rates and a recent slowdown in the pace of fertility decline. Also, the ratio of working-age people to older people is likely to decline substantially in all countries, even those that currently have young Populations.

  • bayesian Population Projections for the united nations
    arXiv: Methodology, 2014
    Co-Authors: Adrian E. Raftery, Leontine Alkema, Patrick Gerland
    Abstract:

    The United Nations regularly publishes Projections of the Populations of all the world's countries broken down by age and sex. These Projections are the de facto standard and are widely used by international organizations, governments and researchers. Like almost all other Population Projections, they are produced using the standard deterministic cohort-component projection method and do not yield statements of uncertainty. We describe a Bayesian method for producing probabilistic Population Projections for most countries which are Projections that the United Nations could use. It has at its core Bayesian hierarchical models for the total fertility rate and life expectancy at birth. We illustrate the method and show how it can be extended to address concerns about the UN's current assumptions about the long-term distribution of fertility. The method is implemented in the R packages bayesTFR, bayesLife, bayesPop and bayesDem.

Bistra Dilkina - One of the best experts on this subject based on the ideXlab platform.

  • a deep learning approach for Population estimation from satellite imagery
    Proceedings of the 1st ACM SIGSPATIAL Workshop on Geospatial Humanities, 2017
    Co-Authors: Caleb Robinson, Fred Hohman, Bistra Dilkina
    Abstract:

    Knowing where people live is a fundamental component of many decision making processes such as urban development, infectious disease containment, evacuation planning, risk management, conservation planning, and more. While bottom-up, survey driven censuses can provide a comprehensive view into the Population landscape of a country, they are expensive to realize, are infrequently performed, and only provide Population counts over broad areas. Population disaggregation techniques and Population projection methods individually address these shortcomings, but also have shortcomings of their own. To jointly answer the questions of "where do people live" and "how many people live there," we propose a deep learning model for creating high-resolution Population estimations from satellite imagery. Specifically, we train convolutional neural networks to predict Population in the USA at a 0.01°x0.01° resolution grid from 1-year composite Landsat imagery. We validate these models in two ways: quantitatively, by comparing our model's grid cell estimates aggregated at a county-level to several US Census county-level Population Projections, and qualitatively, by directly interpreting the model's predictions in terms of the satellite image inputs. We find that aggregating our model's estimates gives comparable results to the Census county-level Population Projections and that the predictions made by our model can be directly interpreted, which give it advantages over traditional Population disaggregation methods. In general, our model is an example of how machine learning techniques can be an effective tool for extracting information from inherently unstructured, remotely sensed data to provide effective solutions to social problems.

  • A Deep Learning Approach for Population Estimation from Satellite Imagery
    arXiv: Artificial Intelligence, 2017
    Co-Authors: Caleb Robinson, Fred Hohman, Bistra Dilkina
    Abstract:

    Knowing where people live is a fundamental component of many decision making processes such as urban development, infectious disease containment, evacuation planning, risk management, conservation planning, and more. While bottom-up, survey driven censuses can provide a comprehensive view into the Population landscape of a country, they are expensive to realize, are infrequently performed, and only provide Population counts over broad areas. Population disaggregation techniques and Population projection methods individually address these shortcomings, but also have shortcomings of their own. To jointly answer the questions of "where do people live" and "how many people live there," we propose a deep learning model for creating high-resolution Population estimations from satellite imagery. Specifically, we train convolutional neural networks to predict Population in the USA at a $0.01^{\circ} \times 0.01^{\circ}$ resolution grid from 1-year composite Landsat imagery. We validate these models in two ways: quantitatively, by comparing our model's grid cell estimates aggregated at a county-level to several US Census county-level Population Projections, and qualitatively, by directly interpreting the model's predictions in terms of the satellite image inputs. We find that aggregating our model's estimates gives comparable results to the Census county-level Population Projections and that the predictions made by our model can be directly interpreted, which give it advantages over traditional Population disaggregation methods. In general, our model is an example of how machine learning techniques can be an effective tool for extracting information from inherently unstructured, remotely sensed data to provide effective solutions to social problems.