Model selection for Lévy measures in diffusion processes with jumps from discrete observations

We deal with parametric inference and selection problems for jump components in discretely observed diffusion processes with jumps. We prepare several competing parametric models for the Lévy measure that might be misspecified, and select the best model from the aspect of information criteria. We construct quasi-information criteria (QIC), which are approximations of the information criteria based on continuous observations.     

失业劳动力的就业渴望强度影响因素实证分析

本文基于重庆直辖市3000失业劳动力家庭人户调查的微观数据,运用排序选择logit模型估计了失业劳动力的人口统计学特征、人力资本、社会背景和社会政策．以及家庭经济状况等因素对其就业渴望强烈程度的影响。本文的政策含义在于：在健全失业保险的前提下,对失业劳动力适当的取消直接货币补偿而代之以非货币的扶持,可能更有利于刺激失业劳动力积极寻找工作和再就业。     

新疆维吾尔自治区死亡水平的演变规律及分布特征分析

本文根据《新疆辉煌50年》及三次人口普查数据,分析了新疆维吾尔自治区历年来死亡率、死亡人数、死亡率模式、平均预期寿命等指标的演变过程及分布特征,试图揭示新疆维吾尔自治区死亡水平的内在变动规律,为计划生育及相关政府部门制定政策提供依据。     

Model selection criteria in beta regression with varying dispersion

We address the issue of model selection in beta regressions with varying dispersion. The model consists of two submodels, namely: for the mean and for the dispersion. Our focus is on the selection of the covariates for each submodel. Our Monte Carlo evidence reveals that the joint selection of covariates for the two submodels is not accurate in finite samples. We introduce two new model selection criteria that explicitly account for varying dispersion and propose a fast two step model selection scheme which is considerably more accurate and is computationally less costly than usual joint model selection. Monte Carlo evidence is presented and discussed. We also present the results of an empirical application.     

A new nonlinear asymmetric cointegration approach using error correction models

In this article, two new powerful tests for cointegration are proposed. The general idea is based on an intuitively appealing extension of the traditional, rather restrictive cointegration concept. In this article, we allow for a nonlinear, but most importantly a different, asymmetric convergence process to account for negative and positive changes in our cointegration approach. Using Monte Carlo simulations we verify, that the estimated size of the first test depends on the unknown value of a signal-to-noise ratio q. However, our second test—which is based on the original ideas of Kanioura and Turner—is more successful and robust in the sense that it works in all of the different evaluated situations. Furthermore it is shown to be more powerful than the traditional residual based Enders and Siklos method. The new optimal test is also applied in an empirical example in order to test for potential nonlinear asymmetric price transmission effects on the Swedish power market. We find that there is a higher propensity for power retailers to rapidly and systematically increase their retail electricity prices subsequent to increases in Nordpool's wholesale prices, than there is for them to reduce their prices subsequent to a drop in wholesale spot prices.     

Bootstrap forecast intervals for asymmetric volatilities via EGARCH model

Bootstrap forecast intervals are developed for volatilities having asymmetric features, which are accounted for by fitting EGARCH models. A Monte-Carlo simulation compares the proposed forecast intervals with those based on GARCH fittings which ignore asymmetry. The comparison reveals substantial advantage of addressing asymmetry through EGARCH fitting over ignoring it as the conventional GARCH forecast. The EGARCH forecast intervals have empirical coverage probabilities closer to the nominal level and/or have shorter average lengths than the GARCH forecast intervals. The finding is also supported by real dataset analysis of Dow–Jones index and financial times stock exchange (FTSE) 100 index.     

Bayesian expectile regression with asymmetric normal distribution

In this paper, we adopt the Bayesian approach to expectile regression employing a likelihood function that is based on an asymmetric normal distribution. We demonstrate that improper uniform priors for the unknown model parameters yield a proper joint posterior. Three simulated data sets were generated to evaluate the proposed method which show that Bayesian expectile regression performs well and has different characteristics comparing with Bayesian quantile regression. We also apply this approach into two real data analysis.     

Bayesian quantile regression for skew-normal linear mixed models

Linear mixed models have been widely used to analyze repeated measures data which arise in many studies. In most applications, it is assumed that both the random effects and the within-subjects errors are normally distributed. This can be extremely restrictive, obscuring important features of within-and among-subject variations. Here, quantile regression in the Bayesian framework for the linear mixed models is described to carry out the robust inferences. We also relax the normality assumption for the random effects by using a multivariate skew-normal distribution, which includes the normal ones as a special case and provides robust estimation in the linear mixed models. For posterior inference, we propose a Gibbs sampling algorithm based on a mixture representation of the asymmetric Laplace distribution and multivariate skew-normal distribution. The procedures are demonstrated by both simulated and real data examples.     

The Conversational Model and Child and Family Counselling: Treating Chronic Complex Trauma in a Systemic Framework

Child and Family Counselling Teams in NSW Community Health Services are expected to provide therapeutic services to children who have experienced complex trauma. However, parental trauma is often overlooked or referred elsewhere. A systemic perspective informed by attachment theory and trauma theory provides the basis for addressing parental trauma in Child and Family Counselling Teams, thereby improving parenting. The Conversational Model is an evidence‐based intervention for chronic complex trauma. The foundations of the Conversational Model and its brief component, Short Term Intensive Psychodynamic Psychotherapy, are outlined and a case study is given to illustrate the usefulness of the intervention in Child and Family Counselling. Highlighted is the importance of addressing systemic trauma through parenting to improve attachment quality, family relationships, and children's function.     

Exploring how the social model of disability can be re-invigorated: in response to Mike Oliver

In his 2013 article in Disability & Society, Oliver recommended that the social model should either be replaced or re-invigorated. I argue here that the social model’s current emphasis reflects the social conditions in which it was introduced, and that the model’s impact on disabled people’s lives would increase if its emphasis was to more accurately reflect the current social conditions in the geographical regions in which it is applied. In order to help foster its re-invigoration, I ask five questions for discussion on the way forward for the social model. I identified my questions through examining published writing on the scope of the social model and on the model’s relationship with other models of disability.