影子银行、地方政府债务及金融发展动态关系研究——基于省级面板VAR模型

基于2004—2017年中国省级面板数据,运用面板向量自回归(PVAR)模型,使用系统GMM估计、脉冲响应函数、方差分解以及格兰杰因果关系检验等方法分析了影子银行、地方政府债务及金融发展之间的动态关系.结果表明:影子银行、地方政府债务与金融发展水平三者之间存在动态耦合关系.在地方政府融资能力受到约束的情况下,影子银行为地方政府提供了多元的融资方式,在增加政府融资能力的同时提升了政府债务水平;而地方政府债务需求显著推动了影子银行规模的快速发展.同时,影子银行过度扩张危害了金融市场的健康发展,降低金融发展水平,继而使地方政府的融资渠道受到约束.但金融发展并不能有效约束影子银行规模,原因在于,政府融资需求是影子银行的主要动力,若不能控制地方政府的借贷行为则无法从源头解决问题.监管机构在去杠杆的过程中,应该综合考虑影子银行与地方政府债务、金融发展之间的动态关系,如此才能够实现预期的政策效果.     

A Bayesian Analysis of Abundance,Trend, and Population Viability for Harbor Seals in Iliamna Lake,Alaska

Harbor seals in Iliamna Lake, Alaska, are a small, isolated population, and one of only two freshwater populations of harbor seals in the world, yet little is known about their abundance or risk for extinction. Bayesian hierarchical models were used to estimate abundance and trend of this population. Observational models were developed from aerial survey and harvest data, and they included effects for time of year and time of day on survey counts. Underlying models of abundance and trend were based on a Leslie matrix model that used prior information on vital rates from the literature. We developed three scenarios for variability in the priors and used them as part of a sensitivity analysis. The models were fitted using Markov chain Monte Carlo methods. The population production rate implied by the vital rate estimates was about 5% per year, very similar to the average annual harvest rate. After a period of growth in the 1980s, the population appears to be relatively stable at around 400 individuals. A population viability analysis assessing the risk of quasi‐extinction, defined as any reduction to 50 animals or below in the next 100 years, ranged from 1% to 3%, depending on the prior scenario. Although this is moderately low risk, it does not include genetic or catastrophic environmental events, which may have occurred to the population in the past, so our results should be applied cautiously.     

伸展构造背景下倾角资料的构造解释

系统总结了在伸展构造背景下，导致反映构造特征的地层倾角模式复杂化的多种因素。沉积相的不同、岩性变化、井眼状况和测井作业时间等多种因素都可能使地层倾角资料复杂化。进一步指出在拉张盆地中，进行倾角资料的构造解释，必须了解其构造样式的多样性和复杂性，解释存在的多解性。重点解剖了铲式断层在横剖面情况下，对于不同构造位置下的倾角模式。详细分析了一个倾角模式对应多种构造样式的倾角构造解释实例。倾角资料的构造解释应是综合解释，既要综合井筒的相关地质资料，又要综合测井与地质知识，还必须考虑综合地震资料。     

Tests for time series of counts based on the probability-generating function

We propose testing procedures for the hypothesis that a given set of discrete observations may be formulated as a particular time series of counts with a specific conditional law. The new test statistics incorporate the empirical probability-generating function computed from the observations. Special emphasis is given to the popular models of integer autoregression and Poisson autoregression. The asymptotic properties of the proposed test statistics are studied under the null hypothesis as well as under alternatives. A Monte Carlo power study on bootstrap versions of the new methods is included as well as real-data examples.     

Price bias and common practice in option pricing

Generally, the semiclosed-form option pricing formula for complex financial models depends on unobservable factors such as stochastic volatility and jump intensity. A popular practice is to use an estimate of these latent factors to compute the option price. However, in many situations this plug-and-play approximation does not yield the appropriate price. This article examines this bias and quantifies its impacts. We decompose the bias into terms that are related to the bias on the unobservable factors and to the precision of their point estimators. The approximated price is found to be highly biased when only the history of the stock price is used to recover the latent states. This bias is corrected when option prices are added to the sample used to recover the states' best estimate. We also show numerically that such a bias is propagated on calibrated parameters, leading to erroneous values. The Canadian Journal of Statistics 48: 8–35; 2020 © 2019 Statistical Society of Canada     

Media Disaster Reporting Effects on Public Risk Perception and Response to Escalating Tornado Warnings: A Natural Experiment

Previous research has evaluated public risk perception and response to a natural hazards in various settings; however, most of these studies were conducted either with a single scenario or after a natural disaster struck. To better understand the dynamic relationships among affect, risk perception, and behavioral intentions related to natural disasters, the current study implements a simulation scenario with escalating weather intensity, and includes a natural experiment allowing comparison of public response before and after a severe tornado event with extensive coverage by the national media. The current study also manipulated the display of warning information, and investigated whether the warning system display format influences public response. Results indicate that (1) affect, risk perception, and behavioral intention escalated as weather conditions deteriorated, (2) responses at previous stages predicted responses at subsequent stages of storm progression, and (3) negative affect predicted risk perception. Moreover, risk perception and behavioral intention were heightened after exposure to the media coverage of an actual tornado disaster. However, the display format manipulation did not influence behavioral responses. The current study provides insight regarding public perception of predisaster warnings and the influence of exposure to media coverage of an actual disaster event.     

Developing a Broadly Applicable Measure of Risk Perception

Decades of research identify risk perception as a largely intuitive and affective construct, in contrast to the more deliberative assessments of probability and consequences that form the foundation of risk assessment. However, a review of the literature reveals that many of the risk perception measures employed in survey research with human subjects are either generic in nature, not capturing any particular affective, probabilistic, or consequential dimension of risk; or focused solely on judgments of probability. The goal of this research was to assess a multidimensional measure of risk perception across multiple hazards to identify a measure that will be broadly useful for assessing perceived risk moving forward. Our results support the idea of risk perception being multidimensional, but largely a function of individual affective reactions to the hazard. We also find that our measure of risk perception holds across multiple types of hazards, ranging from those that are behavioral in nature (e.g., health and safety behaviors), to those that are technological (e.g., pollution), or natural (e.g., extreme weather). We suggest that a general, unidimensional measure of risk may accurately capture one's perception of the severity of the consequences, and the discrete emotions that are felt in response to those potential consequences. However, such a measure is not likely to capture the perceived probability of experiencing the outcomes, nor will it be as useful at understanding one's motivation to take mitigation action.     

Reducing sample size needed for cox-proportional hazards model analysis using more efficient sampling method

Abstract

In general, survival data are time-to-event data, such as time to death, time to appearance of a tumor, or time to recurrence of a disease. Models for survival data have frequently been based on the proportional hazards model, proposed by Cox. The Cox model has intensive application in the field of social, medical, behavioral and public health sciences. In this paper we propose a more efficient sampling method of recruiting subjects for survival analysis. We propose using a Moving Extreme Ranked Set Sampling (MERSS) scheme with ranking based on an easy-to-evaluate baseline auxiliary variable known to be associated with survival time. This paper demonstrates that this approach provides a more powerful testing procedure as well as a more efficient estimate of hazard ratio than that based on simple random sampling (SRS). Theoretical derivation and simulation studies are provided. The Iowa 65+ Rural study data are used to illustrate the methods developed in this paper.     

政府减排目标、产业结构与污染减排

政府是污染减排的重要主体,了解其行为对污染减排的作用对提升污染减排效果及实现中国绿色发展具有重要意义。创造性地将政府减排目标纳入研究框架,构建面板门槛模型,从工业污染排放总量的角度分析政府减排目标、产业结构、经济规模等因素对地方污染减排的影响,并用工业污染物强度进行稳健性检验。结果显示,政府减排目标对污染减排的作用存在阶段性不同:经济发展初级阶段,政府减排目标对污染排放总量的作用方向为正; 经济发展水平较高时,作用方向为负; 两者之间存在适应性调整阶段,作用方向具有不确定性,但总体由正向作用向负向作用转变。产业结构对工业污染排放总量的作用方向因污染物种类而异,经济规模对工业污染排放总量作用方向为正。