日内知情交易概率测度及事件影响研究

在EKOP模型测度PIN的基础上,通过建立时变交易到达率模型,滚动计算日内知情交易概率,并将PIN测度与事件研究相结合,对比了不同规模公司股票在事件日前后PIN的变化情况。实证结果表明我国股市存在信息泄露情况,尤其是小规模公司股票在公告日前一天知情交易明显增加;前一笔知情交易对当前非知情交易有削弱作用,但对当前知情交易有加强作用,可以进一步解释交易的集簇现象：日内PIN对信息的刻画更加准确,但也发现在公共事件发生后第二天PIN仍会增加,这表明市场上存在信息的学习者,需要对引起PIN增加的原因进行更准确的分析。     

“三驾马车”与福建经济增长的动态关系——基于时变参数状态空间模型的实证研究

基于福建省地区生产总值、消费、投资和净出口数据,构建了揭示"三驾马车"与福建经济增长间动态关系的变参数状态空间模型,分析了其动态变化的轨迹与趋势。实证结果表明:"三驾马车"对福建经济增长的拉动作用具有明显的阶段性特征,虽然投资的拉动作用一直超过消费和净出口,但2005年后消费的拉动作用在稳步增强而投资的拉动作用在逐年减弱,说明福建经济对投资的依赖性正在下降,经济转型早已开始并且趋势稳定,正在稳步进入可持续的发展轨道,政府无需做过多的干预。     

金融结构对产业结构影响的时变特征研究——基于SUR和TVPSS模型的实证检验

金融结构与产业结构的关系一直是学术界的研究热点.文章利用中国1998—2017年的年度数据,构建似不相关回归模型从金融结构规模、效率及深化的角度分析金融结构对产业结构合理化和高级化的影响,建立时变参数状态空间模型描绘了金融结构对产业结构合理化和高级化的动态冲击.实证分析结果表明:金融结构规模提高产业结构合理化水平,促进了产业结构高级化;金融结构效率提高产业结构合理化水平,抑制了产业结构高级化;金融结构深化降低产业结构合理化水平,促进了产业结构高级化.金融结构规模、金融结构效率及金融结构深化对产业结构合理化和高级化的冲击均呈现出时变特征;金融结构对产业结构合理化的影响滞后于其对产业结构高级化的影响.金融结构对产业结构的冲击波幅呈现出前期波动大、后期较为平缓的状态,部分金融结构变量对产业结构的动态冲击呈现出"长尾"现象.当前的中国金融结构已经不适合当前的产业结构,需调整金融结构,以提升产业结构合理化水平和高级化水平.     

基于时变参数的中国总量生产函数估计

总量生产函数是宏观经济增长分析和技术进步定量化测算的基础,传统方法估算总量生产函数具有很多不足之处。利用1952—2005年中国宏观经济数据,建立时变参数模型,应用卡尔曼滤波算法,对我国1952—2005年间历年的总量生产函数进行了估算。研究结果表明:利用时变参数估算生产函数较好地捕捉到了经济政策的重大事件对于总量生产函数的影响,可能是理论与实际情况更加接近的一种拟合。我国技术进步和资本、劳动的产出弹性三者相互影响,呈同方向变化,并且技术进步对资本的产出弹性影响较大,对劳动的产出弹性影响较小,1978—2005年间我国属于节约资本型技术进步。     

The Influence of Career Planning on Career Thoughts in STEM‐Interested Undergraduates

Undergraduate career planning courses have shown efficacy in decreasing students’ negative career thoughts; however, universities have minimally applied these courses to science, technology, engineering, and math (STEM) populations. This study compared the influence of a STEM‐focused career planning course for undecided STEM students with a seminar course for decided STEM majors. An analysis of covariance with covariate adjustment revealed that undecided career planning students had lower adjusted mean scores on a measure of negative career thinking than the decided STEM majors after the first semester of college. The results provide support for the efficacy of STEM‐focused career planning courses and measuring negative career thoughts with STEM undergraduates.     

Estimation in capture–recapture models when covariates are subject to measurement errors and missing data

For capture–recapture models when covariates are subject to measurement errors and missing data, a set of estimating equations is constructed to estimate population size and relevant parameters. These estimating equations can be solved by an algorithm similar to the EM algorithm. The proposed method is also applicable to the situation when covariates with no measurement errors have missing data. Simulation studies are used to assess the performance of the proposed estimator. The estimator is also applied to a capture–recapture experiment on the bird species Prinia flaviventris in Hong Kong. The Canadian Journal of Statistics 37: 645–658; 2009 © 2009 Statistical Society of Canada     

Effects of unmeasured heterogeneity in the linear transformation model for censored data

We investigate the effect of unobserved heterogeneity in the context of the linear transformation model for censored survival data in the clinical trials setting. The unobserved heterogeneity is represented by a frailty term, with unknown distribution, in the linear transformation model. The bias of the estimate under the assumption of no unobserved heterogeneity when it truly is present is obtained. We also derive the asymptotic relative efficiency of the estimate of treatment effect under the incorrect assumption of no unobserved heterogeneity. Additionally we investigate the loss of power for clinical trials that are designed assuming the model without frailty when, in fact, the model with frailty is true. Numerical studies under a proportional odds model show that the loss of efficiency and the loss of power can be substantial when the heterogeneity, as embodied by a frailty, is ignored. An erratum to this article can be found at     

Temporal prediction of future state occupation in a multistate model from high-dimensional baseline covariates via pseudo-value regression

In many complex diseases such as cancer, a patient undergoes various disease stages before reaching a terminal state (say disease free or death). This fits a multistate model framework where a prognosis may be equivalent to predicting the state occupation at a future time t. With the advent of high-throughput genomic and proteomic assays, a clinician may intent to use such high-dimensional covariates in making better prediction of state occupation. In this article, we offer a practical solution to this problem by combining a useful technique, called pseudo-value (PV) regression, with a latent factor or a penalized regression method such as the partial least squares (PLS) or the least absolute shrinkage and selection operator (LASSO), or their variants. We explore the predictive performances of these combinations in various high-dimensional settings via extensive simulation studies. Overall, this strategy works fairly well provided the models are tuned properly. Overall, the PLS turns out to be slightly better than LASSO in most settings investigated by us, for the purpose of temporal prediction of future state occupation. We illustrate the utility of these PV-based high-dimensional regression methods using a lung cancer data set where we use the patients’ baseline gene expression values.     

The extended GWMA control chart

This study extends the generally weighted moving average (GWMA) control chart by imitating the double exponentially weighted moving average (DEWMA) technique. The proposed chart is called the double generally weighted moving average (DGWMA) control chart. Simulation is employed to evaluate the average run length characteristics of the GWMA, DEWMA and DGWMA control charts. An extensive comparison of these control charts reveals that the DGWMA control chart with time-varying control limits is more sensitive than the GWMA and the DEWMA control charts for detecting medium shifts in the mean of a process when the shifts are between 0.5 and 1.5 standard deviations. Additionally, the GWMA control chart performs better when the mean shifts are below the 0.5 standard deviation, and the DEWMA control performs better when the mean shifts are above the 1.5 standard deviation. The design of the DGWMA control chart is also discussed.     

Specification and testing of multiplicative time-varying GARCH models with applications

In this article, we develop a specification technique for building multiplicative time-varying GARCH models of Amado and Teräsvirta (2008, 2013). The variance is decomposed into an unconditional and a conditional component such that the unconditional variance component is allowed to evolve smoothly over time. This nonstationary component is defined as a linear combination of logistic transition functions with time as the transition variable. The appropriate number of transition functions is determined by a sequence of specification tests. For that purpose, a coherent modelling strategy based on statistical inference is presented. It is heavily dependent on Lagrange multiplier type misspecification tests. The tests are easily implemented as they are entirely based on auxiliary regressions. Finite-sample properties of the strategy and tests are examined by simulation. The modelling strategy is illustrated in practice with two real examples: an empirical application to daily exchange rate returns and another one to daily coffee futures returns.