scholarly journals Probabilistic Projection of Subnational Life Expectancy

2021 ◽  
Vol 37 (3) ◽  
pp. 591-610
Author(s):  
Hana Ševčíková ◽  
Adrian E. Raftery
Keyword(s):  
Life Expectancy ◽  
Probabilistic Method ◽  
Autoregressive Process ◽  
Bayesian Hierarchical ◽  
Probabilistic Forecasts ◽  
Out Of Sample ◽  
Scaling Methods ◽  
Simple Scaling ◽  
National Life ◽  
Probabilistic Projection

Abstract Projecting mortality for subnational units, or regions, is of great interest to practicing demographers. We seek a probabilistic method for projecting subnational life expectancy that is based on the national Bayesian hierarchical model used by the United Nations, and at the same time is easy to use. We propose three methods of this kind. Two of them are variants of simple scaling methods. The third method models life expectancy for a region as equal to national life expectancy plus a region-specific stochastic process which is a heteroskedastic first-order autoregressive process (AR(1)), with a variance that declines to a constant as life expectancy increases. We apply our models to data from 29 countries. In an out-of-sample comparison, the proposed methods outperformed other comparative methods and were well calibrated for individual regions. The AR (1) method performed best in terms of crossover patterns between regions. Although the methods work well for individual regions, there are some limitations when evaluating within-country variation. We identified four countries for which the AR(1) method either underestimated or overestimated the predictive between-region within-country standard deviation. However, none of the competing methods work better in this regard than the AR(1) method. In addition to providing the full distribution of subnational life expectancy, the methods can be used to obtain probabilistic forecasts of age-specific mortality rates.

scholarly journals The fiscal value of human lives lost from coronavirus disease (COVID-19) in China

2020 ◽  
Author(s):  
Joses M. Kirigia ◽  
Rosenabi Deborah Karimi Muthuri
Keyword(s):  
Life Expectancy ◽  
Economic Model ◽  
Discount Rates ◽  
Average Life ◽  
Average Life Expectancy ◽  
Specific Objective ◽  
National Life

Abstract Objective: According to the WHO coronavirus disease (COVID-19) situation report 35, as of 24 th February 2020, there was a total of 77,262 confirmed COVID-19 cases in China. That included 2,595 deaths. The specific objective of this study was to estimate the fiscal value of human lives lost due to COVID-19 in China as of 24 th February 2020. Results: The deaths from COVID-19 had a discounted (at 3%) total fiscal value of Int$ 924,346,795 in China. Out of which, 63.2% was borne by people aged 25-49 years, 27.8% by people aged 50-64 years, and 9.0% by people aged 65 years and above. The average fiscal value per death was Int$ 356,203. Re-estimation of the economic model alternately with 5% and 10 discount rates led to a reduction in the expected total fiscal value by 21.3% and 50.4%, respectively. Furthermore, the re-estimation of the economic model using the world’s highest average life expectancy of 87.1 years (which is that of Japanese females), instead of the national life expectancy of 76.4 years, increased the total fiscal value by Int$ 229,456,430 (24.8%).

Bayesian hierarchical models for predicting individual performance in soccer

2018 ◽  
Vol 14 (3) ◽  
pp. 143-157
Author(s):  
Leonardo Egidi ◽  
Jonah Gabry
Keyword(s):  
Hierarchical Models ◽  
Predictive Accuracy ◽  
Individual Performance ◽  
Missing Observations ◽  
Posterior Distributions ◽  
Training Set ◽  
Bayesian Hierarchical ◽  
Posterior Predictive Checks ◽  
Modeling And Prediction ◽  
Out Of Sample

Abstract Although there is no consensus on how to measure and quantify individual performance in any sport, there has been less development in this area for soccer than for other major sports. And only once this measurement is defined, does modeling for predictive purposes make sense. We use the player ratings provided by a popular Italian fantasy soccer game as proxies for the players’ performance; we discuss the merits and flaws of a variety of hierarchical Bayesian models for predicting these ratings, comparing the models on their predictive accuracy on hold-out data. Our central goals are to explore what can be accomplished with a simple freely available dataset comprising only a few variables from the 2015–2016 season in the top Italian league, Serie A, and to focus on a small number of interesting modeling and prediction questions that arise. Among these, we highlight the importance of modeling the missing observations and we propose two models designed for this task. We validate our models through graphical posterior predictive checks and we provide out-of-sample predictions for the second half of the season, using the first half as a training set. We use Stan to sample from the posterior distributions via Markov chain Monte Carlo.

Oil price forecasting using probabilistic projection of the United States dollar

2011 ◽  
Vol 51 (1) ◽  
pp. 411
Author(s):  
Noll Moriarty
Keyword(s):  
United States ◽  
Commodity Prices ◽  
The United States ◽  
Oil Price ◽  
Petroleum Exploration ◽  
Capital Expenditures ◽  
Medium Term ◽  
United States Dollar ◽  
Probabilistic Forecasts ◽  
Probabilistic Projection

Accurate forecasts for medium-term commodity prices are essential for resource companies committing to large capital expenditures. The inaccuracy of conventional forecasting methods is well known because they tend to be extrapolations of the current price trend. The inevitable reversal catches many by surprise. This paper demonstrates that medium-term (2–5 years) commodity prices are not strongly linked to economic health and commodity demand-supply, but are instead inversely controlled by supply-demand for the United States dollar (USD) and consequent valuation. P90, P50 and P10 projection bounds for future valuation of the USD are presented based on the successful probabilistic techniques of the petroleum exploration industry. This allows probabilistic projections for the oil price, which is inversely related to the USD valuation. I show that the USD is significantly undervalued at present. Probabilistic projection of the USD valuation indicates that likely appreciation will put downward pressure on commodity prices for the next 2–5 years. If the USD premise is correct, likely appreciation of the dollar during the next 2–5 years will hold stable, or even decrease, oil price to around USD $50 BBL. This is a contrary expectation to most forecasts—one which, if it eventuates, should give cause for reflection before committing to large capital expenditures. Further investigation could examine the extent to which the USD valuation can be modelled as a fractal phenomenon. If so, it would mean the USD valuation is not driven by conventional economic fundamentals; instead, it is a semi-random number series with serial correlation. If true, probabilistic forecasts of the USD can be significantly improved, hence that of medium-term commodity prices.

scholarly journals Earnings implications of person years lost life expectancy among Canada's aboriginal peoples

2003 ◽  
Vol 30 (2) ◽  
pp. 271
Author(s):  
Paul S. Maxim ◽  
Jerry P. White ◽  
Stephen Obeng Gyimah ◽  
Daniel Beavon
Keyword(s):  
General Population ◽  
Life Expectancy ◽  
Economic Consequences ◽  
Aboriginal Peoples ◽  
Differential Mortality ◽  
Labor Income ◽  
Aboriginal Community ◽  
Aboriginal Population ◽  
National Life ◽  
Mortality Differential

Overall, Canada has one of the world’s highest national life expectancies. This benefit is not shared by Canada’s aboriginal population, however, which has a life expectancy approximately seven years less than the general population. The Aboriginal population also differs in that it has a higher fertility rate and higher mortality rates among infants and young adults. One of the consequences of the mortality differential is that the number of person years of lost life (PYLL) expectancy is large for the Aboriginal community in comparison to the general population. While several studies have focused on the causes of differential mortality, this study examines some of the socio-economic consequences of differences in PYLL. Examining wage labor income, for example, we determine that the PYLL differential translates into an expected wage and salary loss of approximately $1.56 billion.

scholarly journals Empirical prediction intervals improve energy forecasting

2017 ◽  
Vol 114 (33) ◽  
pp. 8752-8757 ◽  
Author(s):  
Lynn H. Kaack ◽  
Jay Apt ◽  
M. Granger Morgan ◽  
Patrick McSharry
Keyword(s):  
Energy Information Administration ◽  
Forecast Errors ◽  
Energy Forecasting ◽  
Empirical Prediction ◽  
Probabilistic Forecasts ◽  
Uncertainty Estimates ◽  
Gaussian Density ◽  
Density Forecasting ◽  
Out Of Sample ◽  
Empirical Density

Hundreds of organizations and analysts use energy projections, such as those contained in the US Energy Information Administration (EIA)’s Annual Energy Outlook (AEO), for investment and policy decisions. Retrospective analyses of past AEO projections have shown that observed values can differ from the projection by several hundred percent, and thus a thorough treatment of uncertainty is essential. We evaluate the out-of-sample forecasting performance of several empirical density forecasting methods, using the continuous ranked probability score (CRPS). The analysis confirms that a Gaussian density, estimated on past forecasting errors, gives comparatively accurate uncertainty estimates over a variety of energy quantities in the AEO, in particular outperforming scenario projections provided in the AEO. We report probabilistic uncertainties for 18 core quantities of the AEO 2016 projections. Our work frames how to produce, evaluate, and rank probabilistic forecasts in this setting. We propose a log transformation of forecast errors for price projections and a modified nonparametric empirical density forecasting method. Our findings give guidance on how to evaluate and communicate uncertainty in future energy outlooks.

scholarly journals WOMBAT v1.0: A fully Bayesian global flux-inversion framework

2021 ◽  
Author(s):  
Andrew Zammit-Mangion ◽  
Michael Bertolacci ◽  
Jenny Fisher ◽  
Ann Stavert ◽  
Matthew L. Rigby ◽  
...  
Keyword(s):  
Transport Model ◽  
Atmospheric Transport ◽  
Trace Gases ◽  
Chem Phys ◽  
Remotely Sensed Data ◽  
Bayesian Hierarchical ◽  
Correlated Error ◽  
Out Of Sample ◽  
Fully Bayesian ◽  
Observing System Simulation Experiment

Abstract. WOMBAT (the WOllongong Methodology for Bayesian Assimilation of Trace-gases) is a fully Bayesian hierarchical statistical framework for flux inversion of trace gases from flask, in situ, and remotely sensed data. WOMBAT extends the conventional Bayesian-synthesis framework through the consideration of a correlated error term, the capacity for online bias correction, and the provision of uncertainty quantification on all unknowns that appear in the Bayesian statistical model. We show, in an observing system simulation experiment (OSSE), that these extensions are crucial when the data are indeed biased and have errors that are spatio-temporally correlated. Using the GEOS-Chem atmospheric transport model, we show that WOMBAT is able to obtain posterior means and variances on non-fossil-fuel CO2 fluxes from Orbiting Carbon Observatory-2 (OCO-2) data that are comparable to those from the Model Intercomparison Project (MIP) reported in Crowell et al. (2019, Atmos. Chem. Phys., vol. 19). We also find that WOMBAT's predictions of out-of-sample retrievals obtained from the Total Column Carbon Observing Network are, for the most part, more accurate than those made by the MIP participants.

scholarly journals Intra-Hour Forecasts of Solar Power and Ramp Events †

2018 ◽  
Author(s):  
Mohamed Abuella ◽  
Badrul Chowdhury
Keyword(s):  
United States ◽  
Power Systems ◽  
Solar Power ◽  
The United States ◽  
Post Processing ◽  
Processing Approach ◽  
Probabilistic Forecasts ◽  
Out Of Sample ◽  
Sample Test ◽  
Ramp Events

In this study an adjusting post-processing approach is implemented for improving intra-hourly forecasts of solar power and ramp events of PV solar power systems at different locations in the United States. This study also serves as an out-of-sample test to evaluate the performance of the adjusting approach with different locations and timescales. Thus, various individual intra-hourly forecasts of solar power are combined and adjusted by applying the adjusting approach. Both point and probabilistic forecasts of solar power are included. After that, solar power ramp event forecasting by the adjusting approach is carried out.

Sign in / Sign up

Export Citation Format

Share Document