基于GARCH族模型的企业债信用价差动态过程研究

文章着眼于企业债信用价差时间序列,致力于确定和描述不同期限企业债信用价差的动态变化过程.实证研究结果有助于理解企业债信用价差的时序过程,帮助投资者和监管者分析和预测企业债的信用风险及其变化,以做出正确的投资决策和监管政策.     

Back to basics: explaining sample size in outcome trials,are statisticians doing a thorough job?

Time to event outcome trials in clinical research are typically large, expensive and high‐profile affairs. Such trials are commonplace in oncology and cardiovascular therapeutic areas but are also seen in other areas such as respiratory in indications like chronic obstructive pulmonary disease. Their progress is closely monitored and results are often eagerly awaited. Once available, the top line result is often big news, at least within the therapeutic area in which it was conducted, and the data are subsequently fully scrutinized in a series of high‐profile publications. In such circumstances, the statistician has a vital role to play in the design, conduct, analysis and reporting of the trial. In particular, in drug development it is incumbent on the statistician to ensure at the outset that the sizing of the trial is fully appreciated by their medical, and other non‐statistical, drug development team colleagues and that the risk of delivering a statistically significant but clinically unpersuasive result is minimized. The statistician also has a key role in advising the team when, early in the life of an outcomes trial, a lower than anticipated event rate appears to be emerging. This paper highlights some of the important features relating to outcome trial sample sizing and makes a number of simple recommendations aimed at ensuring a better, common understanding of the interplay between sample size and power and the final result required to provide a statistically positive and clinically persuasive outcome. Copyright © 2009 John Wiley & Sons, Ltd.     

Efficacy of programmes for the unemployed: discrete time modelling of duration data from a matched-comparison study

An evaluation is described of two UK Government programmes for the long-term unemployed in Great Britain, Employment Training and Employment Action, using discrete time hazard modelling of event histories. The study design employed a closely matched comparison group and carefully chosen control variables to minimize the effect of selection bias on conclusions. The effect of unobserved heterogeneity is investigated by using some standard random effect model formulations.     

Geometric ergodicity of nonlinear autoregressive models with changing conditional variances

The authors give easy‐to‐check sufficient conditions for the geometric ergodicity and the finiteness of the moments of a random process x_t = ?(x_t‐1,…, x_t‐p) + ?_tσ(x_t‐1,…, x_t‐q) in which ?: R^p → R, σ R^q → R and (?_t) is a sequence of independent and identically distributed random variables. They deduce strong mixing properties for this class of nonlinear autoregressive models with changing conditional variances which includes, among others, the ARCH(p), the AR(p)‐ARCH(p), and the double‐threshold autoregressive models.     

Detecting active effects in unreplicated designs

Unreplicated factorial designs pose a difficult problem in analysis because there are no degrees of freedom left to estimate the error. Daniel [Technometrics 1 (1959), pp. 311-341] proposed an ingenious graphical method that does not require σ to be estimated. Here we try to put Daniel's method into a formal framework and lift the subjectiveness that carries. A simulation study has been conducted that shows that the proposed method behaves better than Lenth's [Technometrics 31 (1989), pp. 469-473] popular method.     

Bootstrapping data with multiple levels of variation

The authors consider general estimators for the mean and variance parameters in the random effect model and in the transformation model for data with multiple levels of variation. They show that these estimators have different distributions under the two models unless all the variables have Gaussian distributions. They investigate the asymptotic properties of bootstrap procedures designed for the two models. They also report simulation results and illustrate the bootstraps using data on red spruce trees.     

Inference for general parametric functions in box‐Cox‐type transformation models

The authors propose a simple but general method of inference for a parametric function of the Box‐Cox‐type transformation model. Their approach is built upon the classical normal theory but takes parameter estimation into account. It quickly leads to test statistics and confidence intervals for a linear combination of scaled or unsealed regression coefficients, as well as for the survivor function and marginal effects on the median or other quantité functions of an original response. The authors show through simulations that the finite‐sample performance of their method is often superior to the delta method, and that their approach is robust to mild departures from normality of error distributions. They illustrate their approach with a numerical example.     

中国城市化环境污染效应研究:城市边界拓展视角的考察

城市内部非核心区域的向外扩张构成中国在过去20多年城市化进程快速推进的一个典型特征,由此体现的城市空间格局演变使得城市化的环境污染效应变得复杂。基于我国273个地级市1999—2020年面板数据,从城市内部非建成区面积反映的城市边界区域扩张视角探讨了城市化进程的环境污染效应及其区域异质性作用规律。研究发现:城市边界扩张的环境污染效应呈U形关系,即城市边界扩张在初期有助于降低环境污染,但边界的过度扩张则显著不利于环境改善。特别地,城市边界扩张更易在我国三、四线城市和中西部区域城市呈现较为明显的环境污染效应。结合城市边界扩张的异质环境污染影响,认为我国高等级城市可以保持适度空间外拓,同时重点优化拓展空间各类功能区的布局并加强与原核心城区的整合;对城市边界扩张过快的低等级城市,需要限制其无序扩张并加强扩张区域的综合治理。针对城市边界扩展污染效应表现出的U形关系特点,认为关键是通过人-地空间耦合的增强、更严格环境规制的实施、构建城市边界拓展区域经济收益多渠道环境污染补偿机制等,降低城市边界向外扩张越过环境污染效应拐点的可能性,并减缓其越过污染效应拐点的不利影响。     

No mood effects in the field: The case of car inspections

We study mood effect in the field to measure its economic impact and address shortcomings in the existing literature, which typically uses one single mood proxy and ignores selection effects. Using over 50 million car inspections in Sweden and England and multiple mood proxies, we study whether car inspectors are more lenient on good mood days and if car owners self-select into those days. We find evidence of a “Friday effect” in England and a small selection bias, but no support for consistent mood effect. Our findings highlight the importance of considering the expectations of rational actors who may exploit mood effect and the need to study mood in the field using different settings and multiple proxies to avoid hasty conclusions.     

Warranted inferences of mediation and possibility of a threshold effect: Verifying the mediating effects of anger in situational crisis communication theory

Scholars have called for communication research to verify the causal claims of mediation models from a research design perspective, instead of only proving mediation statistically. This study validates whether and how anger mediates the causal effects of crisis types on publics’ responses in Situational Crisis Communication Theory (SCCT), including reputation, negative word-of-mouth intention, and purchase intention in China. Two experiments were conducted based on the experimental-causal-chain design. Results in Study 1 demonstrate that the causal relationships between three crisis types and publics’ emotional and other responses in China are consistent with findings in Western contexts. In Study 2, the results of a 2 (anger: low, high) x 3 (crisis types: victim crisis, accidental crisis, preventable crisis) factorial experiment reveal significant mediating effects of anger on publics’ responses in the victim and accidental crisis conditions, but not for preventable crises. This novel finding suggests the possibility of a threshold effect of anger in the mediating process. Specifically, anger has a mediating effect on publics’ responses when it is below the high-anger threshold. However, anger may no longer be the mediator when it exceeds this threshold. This finding empirically challenges the common assumption that emotions have a linear relationship with publics’ responses, thus offering a new research avenue and deeper understanding of how emotions function in crises. Therefore, this research serves as a pioneer, calling for future studies to validate other theories involving mediation to yield fruitful insights.