The relationship between lifetime health trajectories and socioeconomic attainment in middle age

A large literature demonstrates the direct and indirect influence of health on socioeconomic attainment, and reveals the ways in which health and socioeconomic background simultaneously and dynamically affect opportunities for attainment and mobility. Despite an increasing understanding of the effects of health on social processes, research to date remains limited in its conceptualization and measurement of the temporal dimensions of health, especially in the presence of socioeconomic circumstances that covary with health over time. Guided by life course theory, we use data from the British National Child Development Study, an ongoing panel study of a cohort born in 1958, to examine the association between lifetime health trajectories and socioeconomic attainment in middle age. We apply finite mixture modeling to identify distinct trajectories of health that simultaneously account for timing, duration and stability. Moreover, we employ propensity score weighting models to account for the presence of time-varying socioeconomic factors in estimating the impact of health trajectories. We find that, when poor health is limited to the childhood years, the disadvantage in socioeconomic attainment relative to being continuously healthy is either insignificant or largely explained by time-varying socioeconomic confounders. The socioeconomic impact of continuously deteriorating health over the life course is more persistent, however. Our results suggest that accounting for the timing, duration and stability of poor health throughout both childhood and adulthood is important for understanding how health works to produce social stratification. In addition, the findings highlight the importance of distinguishing between confounding and mediating effects of time-varying socioeconomic circumstances.     

Time series models with asymmetric Laplace innovations

We propose autoregressive moving average (ARMA) and generalized autoregressive conditional heteroscedastic (GARCH) models driven by asymmetric Laplace (AL) noise. The AL distribution plays, in the geometric-stable class, the analogous role played by the normal in the alpha-stable class, and has shown promise in the modelling of certain types of financial and engineering data. In the case of an ARMA model we derive the marginal distribution of the process, as well as its bivariate distribution when separated by a finite number of lags. The calculation of exact confidence bands for minimum mean-squared error linear predictors is shown to be straightforward. Conditional maximum likelihood-based inference is advocated, and corresponding asymptotic results are discussed. The models are particularly suited for processes that are skewed, peaked, and leptokurtic, but which appear to have some higher order moments. A case study of a fund of real estate returns reveals that AL noise models tend to deliver a superior fit with substantially less parameters than normal noise counterparts, and provide both a competitive fit and a greater degree of numerical stability with respect to other skewed distributions.     

Quantile Regression for Location‐Scale Time Series Models with Conditional Heteroscedasticity

This paper considers quantile regression for a wide class of time series models including autoregressive and moving average (ARMA) models with asymmetric generalized autoregressive conditional heteroscedasticity errors. The classical mean‐variance models are reinterpreted as conditional location‐scale models so that the quantile regression method can be naturally geared into the considered models. The consistency and asymptotic normality of the quantile regression estimator is established in location‐scale time series models under mild conditions. In the application of this result to ARMA‐generalized autoregressive conditional heteroscedasticity models, more primitive conditions are deduced to obtain the asymptotic properties. For illustration, a simulation study and a real data analysis are provided.     

Mixed-effects Models with Skewed Distributions for Time-varying Decay Rate in HIV Dynamics

After initiation of treatment, HIV viral load has multiphasic changes, which indicates that the viral decay rate is a time-varying process. Mixed-effects models with different time-varying decay rate functions have been proposed in literature. However, there are two unresolved critical issues: (i) it is not clear which model is more appropriate for practical use, and (ii) the model random errors are commonly assumed to follow a normal distribution, which may be unrealistic and can obscure important features of within- and among-subject variations. Because asymmetry of HIV viral load data is still noticeable even after transformation, it is important to use a more general distribution family that enables the unrealistic normal assumption to be relaxed. We developed skew-elliptical (SE) Bayesian mixed-effects models by considering the model random errors to have an SE distribution. We compared the performance among five SE models that have different time-varying decay rate functions. For each model, we also contrasted the performance under different model random error assumptions such as normal, Student-t, skew-normal, or skew-t distribution. Two AIDS clinical trial datasets were used to illustrate the proposed models and methods. The results indicate that the model with a time-varying viral decay rate that has two exponential components is preferred. Among the four distribution assumptions, the skew-t and skew-normal models provided better fitting to the data than normal or Student-t model, suggesting that it is important to assume a model with a skewed distribution in order to achieve reasonable results when the data exhibit skewness.     

Robust Inference for Near-Unit Root Processes with Time-Varying Error Variances

The autoregressive Cauchy estimator uses the sign of the first lag as instrumental variable (IV); under independent and identically distributed (i.i.d.) errors, the resulting IV t-type statistic is known to have a standard normal limiting distribution in the unit root case. With unconditional heteroskedasticity, the ordinary least squares (OLS) t statistic is affected in the unit root case; but the paper shows that, by using some nonlinear transformation behaving asymptotically like the sign as instrument, limiting normality of the IV t-type statistic is maintained when the series to be tested has no deterministic trends. Neither estimation of the so-called variance profile nor bootstrap procedures are required to this end. The Cauchy unit root test has power in the same 1/T neighborhoods as the usual unit root tests, also for a wide range of magnitudes for the initial value. It is furthermore shown to be competitive with other, bootstrap-based, robust tests. When the series exhibit a linear trend, however, the null distribution of the Cauchy test for a unit root becomes nonstandard, reminiscent of the Dickey-Fuller distribution. In this case, inference robust to nonstationary volatility is obtained via the wild bootstrap.     

Macro-Economic Factors in Credit Risk Calculations: Including Time-Varying Covariates in Mixture Cure Models

The prediction of the time of default in a credit risk setting via survival analysis needs to take a high censoring rate into account. This rate is because default does not occur for the majority of debtors. Mixture cure models allow the part of the loan population that is unsusceptible to default to be modeled, distinct from time of default for the susceptible population. In this article, we extend the mixture cure model to include time-varying covariates. We illustrate the method via simulations and by incorporating macro-economic factors as predictors for an actual bank dataset.     

巨灾风险对非寿险总产出核算影响的研究

 随着中国保险业的迅猛发展,保险核算在一国经济中变得越来越重要。越来越频繁发生的自然灾害、恐怖袭击等造成的巨大损失对联合国国民账户体系(93SNA)推荐的非寿险服务产出核算产生重大冲击,严格按照93SNA算法计算保险服务价值会导致荒谬的结果。首先本文介绍了国际上几种主要的改进方法,对每种方法的优缺点进行了深入分析。这些方法对我国改善保险核算,尤其是非寿险服务产出核算具有重要的启示意义。其次运用广东省（不含深圳）保险业的数据和期望法对06、07年的总产出进行计算,构建预测模型,并比较分析计算的结果。最后,本文提出对改进我国保险核算的建议。     

Transformed generalized linear models

The estimation of data transformation is very useful to yield response variables satisfying closely a normal linear model. Generalized linear models enable the fitting of models to a wide range of data types. These models are based on exponential dispersion models. We propose a new class of transformed generalized linear models to extend the Box and Cox models and the generalized linear models. We use the generalized linear model framework to fit these models and discuss maximum likelihood estimation and inference. We give a simple formula to estimate the parameter that index the transformation of the response variable for a subclass of models. We also give a simple formula to estimate the r$r$th moment of the original dependent variable. We explore the possibility of using these models to time series data to extend the generalized autoregressive moving average models discussed by Benjamin et al. [Generalized autoregressive moving average models. J. Amer. Statist. Assoc. 98, 214–223]. The usefulness of these models is illustrated in a simulation study and in applications to three real data sets.     

基于ARMA模型的江苏省城乡居民信息消费差距及其预测分析

在1998—2012年江苏省城乡居民信息消费数据的基础上,利用信息消费的倾向、信息消费的系数、信息消费结构等指标对比分析江苏省城乡居民信息消费的现状,发现城乡居民信息消费虽呈现持续增长的良好态势,但两者的差距也显而易见。故通过构建ARMA模型,对两者的差距进行拟合并作出短期预测,实证结果表明城乡居民间的"数字鸿沟"有愈演愈烈的趋势。基于此,在维持城镇居民信息消费示范作用的前提下,应从提升农民信息消费能力、构建完善的农村信息消费环境等方面努力缩小城乡之间的"数字鸿沟"。     

Testing Covariance Stationarity

In this paper, we show that the widely used stationarity tests such as the Kwiatkowski Phillips, Schmidt, and Shin (KPSS) test have power close to size in the presence of time-varying unconditional variance. We propose a new test as a complement of the existing tests. Monte Carlo experiments show that the proposed test possesses the following characteristics: (i) in the presence of unit root or a structural change in the mean, the proposed test is as powerful as the KPSS and other tests; (ii) in the presence of a changing variance, the traditional tests perform badly whereas the proposed test has high power comparing to the existing tests; (iii) the proposed test has the same size as traditional stationarity tests under the null hypothesis of stationarity. An application to daily observations of return on U.S. Dollar/Euro exchange rate reveals the existence of instability in the unconditional variance when the entire sample is considered, but stability is found in subsamples.