Forecasting multiple functional time series in a group structure: an application to mortality’

When modeling sub-national mortality rates, we should consider three features: (1) how to incorporate any possible correlation among sub-populations to potentially improve forecast accuracy through multi-population joint modeling; (2) how to reconcile sub-national 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 one-step-ahead to 15-step-ahead point and interval forecast accuracies of our proposed extension and make recommendations

Phenological response of vegetation to upstream river flow in the Heihe Rive basin by time series analysis of MODIS data

Liquid and solid precipitation is abundant in the high elevation, upper reach of the Heihe River basin in northwestern China. The development of modern irrigation schemes in the middle reach of the basin is taking up an increasing share of fresh water resources, endangering the oasis and traditional irrigation systems in the lower reach. In this study, the response of vegetation in the Ejina Oasis in the lower reach of the Heihe River to the water yield of the upper catchment was analyzed by time series analysis of monthly observations of precipitation in the upper and lower catchment, river streamflow downstream of the modern irrigation schemes and satellite observations of vegetation index. Firstly, remotely sensed NDVI data acquired by Terra-MODIS are used to monitor the vegetation dynamic for a seven years period between 2000 and 2006. Due to cloud-contamination, atmospheric influence and different solar and viewing angles, however, the quality and consistence of time series of remotely sensed NDVI data are degraded. A Fourier Transform method – the Harmonic Analysis of Time Series (HANTS) algorithm – is used to reconstruct cloud- and noise-free NDVI time series data from the Terra-MODIS NDVI dataset. Modification is made on HANTS by adding additional parameters to deal with large data gaps in yearly time series in combination with a Temporal-Similarity-Statistics (TSS) method developed in this study to seek for initial values for the large gap periods. Secondly, the same Fourier Transform method is used to model time series of the vegetation phenology. The reconstructed cloud-free NDVI time series data are used to study the relationship between the water availability (i.e. the local precipitation and upstream water yield) and the evolution of vegetation conditions in Ejina Oasis from 2000 to 2006. Anomalies in precipitation, streamflow, and vegetation index are detected by comparing each year with the average year. The results showed that: the previous year total runoff had a significant relationship with the vegetation growth in Ejina Oasis and that anomalies in the spring monthly runoff of the Heihe River influenced the phenology of vegetation in the entire oasis. Warmer climate expressed by the degree-days showed positive influence on the vegetation phenology in particular during drier years. The time of maximum green-up is uniform throughout the oasis during wetter years, but showed a clear S-N gradient (downstream) during drier years

Forecasting age distribution of death counts: An application to annuity pricing

We consider a compositional data analysis approach to forecasting the age distribution of death counts. Using the age-specific period life-table death counts in Australia obtained from the Human Mortality Database, the compositional data analysis approach produces more accurate one- to 20-step-ahead point and interval forecasts than Lee-Carter method, Hyndman-Ullah method, and two na¨ıve random walk methods. The improved forecast accuracy of period life-table death counts is of great interest to demographers for estimating survival probabilities and life expectancy, and to actuaries for determining temporary annuity prices for various ages and maturities. Although we focus on temporary annuity prices, we consider long-term contracts which make the annuity almost lifetime, in particular when the age at entry is sufficiently high

Grouped multivariate and functional time series forecasting: an application to annuity pricing

Age-specific mortality rates are often disaggregated by different attributes, such as sex, state, ethnic group and socioeconomic status. In making social policies and pricing annuity at national and subnational levels, it is important not only to forecast mortality accurately, but also to ensure that forecasts at the subnational level add up to the forecasts at the national level. This motivates recent developments in grouped functional time series methods (Shang and Hyndman 2017) to reconcile age-specific mortality forecasts. We extend these grouped functional time series forecasting methods to multivariate time series, and apply them to produce point forecasts of mortality rates at older ages, from which fixed-term annuities for different ages and maturities can be priced. Using the regional age-specific mortality rates in Japan obtained from the Japanese Mortality Database, we investigate the one-step-ahead to 15-step-ahead point-forecast accuracy between the independent and grouped forecasting methods. The grouped forecasting methods are shown not only to be useful for reconciling forecasts of age-specific mortality rates at national and subnational levels, but they are also shown to allow improved forecast accuracy. The improved forecast accuracy of mortality rates is of great interest to the insurance and pension industries for estimating annuity prices, in particular at the level of population subgroups, defined by key factors such as sex, region, and socioeconomic grouping

Retiree Mortality Forecasting: A Partial Age-Range or a Full Age-Range Model?

An essential input of annuity pricing is the future retiree mortality. From observed age-specific mortality data, modeling and forecasting can be taken place in two routes. On the one hand, we can first truncate the available data to retiree ages and then produce mortality forecasts based on a partial age-range model. On the other hand, with all available data, we can first apply a full age-range model to produce forecasts and then truncate the mortality forecasts to retiree ages. We investigate the difference in modeling the logarithmic transformation of the central mortality rates between a partial age-range and a full age-range model, using data from mainly developed countries in the Human Mortality Database (2020). By evaluating and comparing the short-term point and interval forecast accuracies, we recommend the first strategy by truncating all available data to retiree ages and then produce mortality forecasts. However, when considering the long-term forecasts, it is unclear which strategy is better since it is more difficult to find a model and parameters that are optimal. This is a disadvantage of using methods based on time series extrapolation for long-term forecasting. Instead, an expectation approach, in which experts set a future target, could be considered, noting that this method has also had limited success in the past

Model confidence sets and forecast combination: an application to age-specific mortality

Background: Model averaging combines forecasts obtained from a range of models, and it often produces more accurate forecasts than a forecast from a single model. Objective: The crucial part of forecast accuracy improvement in using the model averaging lies in the determination of optimal weights from a finite sample. If the weights are selected sub-optimally, this can affect the accuracy of the model-averaged forecasts. Instead of choosing the optimal weights, we consider trimming a set of models before equally averaging forecasts from the selected superior models. Motivated by Hansen et al. (2011), we apply and evaluate the model confidence set procedure when combining mortality forecasts. Data & Methods: The proposed model averaging procedure is motivated by Samuels and Sekkel (2017) based on the concept of model confidence sets as proposed by Hansen et al. (2011) that incorporates the statistical significance of the forecasting performance. As the model confidence level increases, the set of superior models generally decreases. The proposed model averaging procedure is demonstrated via national and sub-national Japanese mortality for retirement ages between 60 and 100+. Results: Illustrated by national and sub-national Japanese mortality for ages between 60 and 100+, the proposed model-average procedure gives the smallest interval forecast errors, especially for males. Conclusion: We find that robust out-of-sample point and interval forecasts may be obtained from the trimming method. By robust, we mean robustness against model misspecification

Measurements of J/psi Decays into 2(pi+pi-)eta and 3(pi+pi-)eta

Based on a sample of 5.8X 10^7 J/psi events taken with the BESII detector, the branching fractions of J/psi--> 2(pi+pi-)eta and J/psi-->3(pi+pi-)eta are measured for the first time to be (2.26+-0.08+-0.27)X10^{-3} and (7.24+-0.96+-1.11)X10^{-4}, respectively.Comment: 11 pages, 6 figure

BESII Detector Simulation

A Monte Carlo program based on Geant3 has been developed for BESII detector simulation. The organization of the program is outlined, and the digitization procedure for simulating the response of various sub-detectors is described. Comparisons with data show that the performance of the program is generally satisfactory.Comment: 17 pages, 14 figures, uses elsart.cls, to be submitted to NIM

Measurement of branching fractions for the inclusive Cabibbo-favored ~K*0(892) and Cabibbo-suppressed K*0(892) decays of neutral and charged D mesons

The branching fractions for the inclusive Cabibbo-favored ~K*0 and Cabibbo-suppressed K*0 decays of D mesons are measured based on a data sample of 33 pb-1 collected at and around the center-of-mass energy of 3.773 GeV with the BES-II detector at the BEPC collider. The branching fractions for the decays D+(0) -> ~K*0(892)X and D0 -> K*0(892)X are determined to be BF(D0 -> \~K*0X) = (8.7 +/- 4.0 +/- 1.2)%, BF(D+ -> ~K*0X) = (23.2 +/- 4.5 +/- 3.0)% and BF(D0 -> K*0X) = (2.8 +/- 1.2 +/- 0.4)%. An upper limit on the branching fraction at 90% C.L. for the decay D+ -> K*0(892)X is set to be BF(D+ -> K*0X) < 6.6%

Study of J/ψ→ωK+K−J/\psi \to \omega K^+K^-

New data are presented on $J/\psi \to \omega K^+K^-$ from a sample of 58M $J/\psi$ events in the upgraded BES II detector at the BEPC. There is a conspicuous signal for $f_0(1710) \to K^+K^-$ and a peak at higher mass which may be fitted with $f_2(2150) \to K\bar K$. From a combined analysis with $\omega \pi ^+ \pi ^-$ data, the branching ratio $BR(f_0(1710)\to\pi\pi)/BR(f_0(1710) \to K\bar K)$ is $< 0.11$ at the 95% confidence level.Comment: 11 pages, 5 figures. Submitted to Phys. Lett.