HEAVY-TAILED-DISTRIBUTED THRESHOLD STOCHASTIC VOLATILITY MODELS IN FINANCIAL TIME SERIES

To capture mean and variance asymmetries and time‐varying volatility in financial time series, we generalize the threshold stochastic volatility (THSV) model and incorporate a heavy‐tailed error distribution. Unlike existing stochastic volatility models, this model simultaneously accounts for uncertainty in the unobserved threshold value and in the time‐delay parameter. Self‐exciting and exogenous threshold variables are considered to investigate the impact of a number of market news variables on volatility changes. Adopting a Bayesian approach, we use Markov chain Monte Carlo methods to estimate all unknown parameters and latent variables. A simulation experiment demonstrates good estimation performance for reasonable sample sizes. In a study of two international financial market indices, we consider two variants of the generalized THSV model, with US market news as the threshold variable. Finally, we compare models using Bayesian forecasting in a value‐at‐risk (VaR) study. The results show that our proposed model can generate more accurate VaR forecasts than can standard models.     

Population constraints on pooled surveys in demographic hazard modeling

In non-experimental research, data on the same population process may be collected simultaneously by more than one instrument. For example, in the present application, two sample surveys and a population birth registration system all collect observations on first births by age and year, while the two surveys additionally collect information on women’s education. To make maximum use of the three data sources, the survey data are pooled and the population data introduced as constraints in a logistic regression equation. Reductions in standard errors about the age and birth-cohort parameters of the regression equation in the order of three-quarters are obtained by introducing the population data as constraints. A halving of the standard errors about the education parameters is achieved by pooling observations from the larger survey dataset with those from the smaller survey. The percentage reduction in the standard errors through imposing population constraints is independent of the total survey sample size.     

Causal inference with generalized structural mean models

Summary.  We estimate cause–effect relationships in empirical research where exposures are not completely controlled, as in observational studies or with patient non-compliance and self-selected treatment switches in randomized clinical trials. Additive and multiplicative structural mean models have proved useful for this but suffer from the classical limitations of linear and log-linear models when accommodating binary data. We propose the generalized structural mean model to overcome these limitations. This is a semiparametric two-stage model which extends the structural mean model to handle non-linear average exposure effects. The first-stage structural model describes the causal effect of received exposure by contrasting the means of observed and potential exposure-free outcomes in exposed subsets of the population. For identification of the structural parameters, a second stage 'nuisance' model is introduced. This takes the form of a classical association model for expected outcomes given observed exposure. Under the model, we derive estimating equations which yield consistent, asymptotically normal and efficient estimators of the structural effects. We examine their robustness to model misspecification and construct robust estimators in the absence of any exposure effect. The double-logistic structural mean model is developed in more detail to estimate the effect of observed exposure on the success of treatment in a randomized controlled blood pressure reduction trial with self-selected non-compliance.     

How Welfare Policies Affect Adolescents' School Outcomes: A Synthesis of Evidence From Experimental Studies

Using data from 8 random assignment studies and employing meta‐analytic techniques, this article provides systematic evidence that welfare and work policies targeted at low‐income parents have small adverse effects on some school outcomes among adolescents ages 12 to 18 years at follow‐up. These adverse effects were observed mostly for school performance outcomes and occurred in programs that required mothers to work or participate in employment‐related activities and those that encouraged mothers to work voluntarily. The most pronounced negative effects on school outcomes occurred for the group of adolescents who had a younger sibling, possibly because of the increased home and sibling care responsibilities they assumed as their mothers increased their employment.     

Individualised Support and Funding: building blocks for capacity building and inclusion

The paradigm in disability supports is shifting away from institutional services and professional control towards self-determination and community involvement of people with disabilities. The assumption that the best way to provide disability supports is for government to give money to agencies or services, rather than directly to people with disabilities and their support networks, is being challenged. This article reports on findings and themes from a Canadian study that investigated individualised funding projects from different parts of the world. Ten of fifteen of the most 'promising initiatives' were selected for more detailed study and analysis. Projects analysed were from Canada, the US, and Australia. Themes emerging from the study included the fact that values and principles mattered, a policy framework provided coherence and equity, infrastructure supports for individuals were separate from service system, the facilitator-broker role differed from case management, allocation of individualised funds was designed to be equitable and account able to the funder and person, and a 'learn as you go' philosophy maximised positive outcomes. This research project demonstrates that individualised support and funding, when embedded in the new paradigm of disability and community, builds capacity of individuals, families, and communities.     

Foster parenting: A developmental model

This paper proposes a developmental framework for foster parents and outlines four distinct growth stages. Such a framework can be of value to program administrators who are required to assess foster parent development during the crucial matching process. To draw a distinction between each developmental stage, specific instrumental tasks and indicators are outlined.     

Psychological Control and Autonomy Granting: Opposite Ends of a Continuum or Distinct Constructs?

This article explores the relationship between parental psychological control and parental autonomy granting, and the relations between these constructs and indicators of adolescent psychosocial functioning, in a sample of 9,564 adolescents from grades 9 to 12. Participants completed a comprehensive parenting questionnaire as well as several measures of psychosocial adjustment. Confirmatory factor analyses of the parenting items revealed discrete factors for psychological control and autonomy granting, suggesting that these are distinct parenting constructs rather than opposite ends of a parental control continuum. Moreover, structural equation modeling showed that these factors were weakly correlated and differentially related to adolescent internalizing symptoms. Findings have implications for future conceptualization and measurement of psychological control and autonomy granting, and for research examining their effects on adolescent development.