Analysis of a continuous-time proportional hazard model using discrete duration data

Many economic duration variables are often available only up to intervals, and not up to exact points. However, continuous time duration models are conceptually superior to discrete ones. Hence, in duration analyses, one faces a situation with discrete data and a continuous model. This paper discusses (i) the asymptotic bias of a conventional approximation procedure in which a discrete duration is treated as an exact observation; and (ii) the efficiency of a correct maximum likelihood estimator which appropriately accounts for the discrete nature of the data.     

Direct and Indirect Causal Effects via Potential Outcomes*

Abstract. The use of the concept of ‘direct’ versus ‘indirect’ causal effects is common, not only in statistics but also in many areas of social and economic sciences. The related terms of ‘biomarkers’ and ‘surrogates’ are common in pharmacological and biomedical sciences. Sometimes this concept is represented by graphical displays of various kinds. The view here is that there is a great deal of imprecise discussion surrounding this topic and, moreover, that the most straightforward way to clarify the situation is by using potential outcomes to define causal effects. In particular, I suggest that the use of principal stratification is key to understanding the meaning of direct and indirect causal effects. A current study of anthrax vaccine will be used to illustrate ideas.     

Two ways of modelling overdispersion in non-normal data

For non-normal data assumed to have distributions, such as the Poisson distribution, which have an a priori dispersion parameter, there are two ways of modelling overdispersion: by a quasi-likelihood approach or with a random-effect model. The two approaches yield different variance functions for the response, which may be distinguishable if adequate data are available. The epilepsy data of Thall and Vail and the fabric data of Bissell are used to exemplify the ideas.     

基于贝叶斯图模型方法的投资组合决策

提出基于贝叶斯思想的图模型结构检测方法:首先给出精度矩阵新的参数化方法,且通过MCMC方法给出算法设计,最后将该图模型方法应用于中国证券市场,研究在牛市和熊市下证券市场六大板块之间的条件相关性.实证结果表明:牛市和熊市下的行业板块图结构存在显著差异,熊市似乎有着更强的相关性.     

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.     

中国宏观经济对沪市的波动溢出效应研究——基于VAR（k）-MGARCH—BEKK（1，1）模型

本文基于VAR（k）-MGARCH—BEKK（1,1）模型和Wald检验探讨了2003—2008年期间的全国银行间同业拆借利率、汇率、企业商品价格指数、工业增加值增长率、M0、宏观景气指数和消费价格指数对沪市的波动溢出效应,结果表明,除了企业商品价格指数和M0之外,其它宏观经济指标的增长率（速度）均对沪市收益率产生显著的、直接的波动溢出效应。     

Adaptive estimation of the conditional cumulative distribution function from current status data

Consider a positive random variable of interest Y depending on a covariate X, and a random observation time T independent of Y given X. Assume that the only knowledge available about Y is its current status at time T  : δ=_I{Y≤T}$\delta ={\mathbb{I}}_{\{Y\le T\}}$ with I$\mathbb{I}$ the indicator function. This paper presents a procedure to estimate the conditional cumulative distribution function F of Y given X   from an independent identically distributed sample of (X,T,δ)$(X,T,\delta )$.     

各省区季度GDP数据质量评估

  国内外学者对我国GDP数据质量的质疑重点已从年度数据转到季度数据,从全国数据转向地方数据。本文通过设计一套较为系统且可操作性强的季度GDP评估指标体系,运用空间面板数据模型对各省区的季度GDP数据质量进行了实证检验。结果表明,整体来看,中国各省区季度GDP同各经济指标的匹配性较好,数据质量较高,并不存在明显的失真现象;从时间上来看,每年一、二季度的GDP存在一定程度的高估,而每年三、四季度的GDP则存在一定程度的低估,但是这种偏差在统计上不显著;分地区来看,尽管一半省区的季度GDP存在一定程度的高估,另一半省区存在一定程度的低估,但大部分省区高估或低估的程度在统计上不显著。文章进一步分析了其中的原因。     

变量误差模型下的代际收入弹性估计：一个文献综述

 代际收入流动性反映了社会的机会平等与经济公正程度,为收入分配研究提供了动态视角,主要由代际收入弹性来度量。对代际收入弹性的估计,主流方法都是建立在变量误差模型基础上的。随着新数据集的使用,研究者对收入动态方程假设进行了修正,从而推动了变量误差模型的发展,反过来变量误差模型的发展促进了代际收入弹性的一致性估计。     

Bayesian Binomial Regression: Predicting Survival at a Trauma Center

Standard methods for analyzing binomial regression data rely on asymptotic inferences. Bayesian methods can be performed using simple computations, and they apply for any sample size. We provide a relatively complete discussion of Bayesian inferences for binomial regression with emphasis on inferences for the probability of “success.” Furthermore, we illustrate diagnostic tools, perform model selection among nonnested models, and examine the sensitivity of the Bayesian methods.