An Evaluation of Behavioral Health Compliance and Microbial Risk Factors on Student Populations Within a High-Density Campus

Abstract

Objective: The aim of this Canadian study was to assess student behavioral response to disease transmission risk, while identifying high microbial deposition/transmission sites. Participants: A student survey was conducted during October 2009. Methods: The methods included a survey of students to assess use of health services, vaccination compliance, and hygiene along with a microbial analysis of potential transmission sites targeting specific residence buildings on campus. Results: Results indicated that most students maintained that they were worried about H1N1 and reported making changes in hygienic behavior, with the majority not planning to be vaccinated. The microbial analysis indicated contamination of fomites in co-ed residences to be higher than either male or female student residences. Conclusions: A consideration of physical space along with behavioral factors is required in order to properly assess risk pathways in the establishment of an evidence-based infection control plan for universities and their contiguous communities.     

HEAVY-TAILED-DISTRIBUTED THRESHOLD STOCHASTIC VOLATILITY MODELS IN FINANCIAL TIME SERIES

To capture mean and variance asymmetries and time‐varying volatility in financial time series, we generalize the threshold stochastic volatility (THSV) model and incorporate a heavy‐tailed error distribution. Unlike existing stochastic volatility models, this model simultaneously accounts for uncertainty in the unobserved threshold value and in the time‐delay parameter. Self‐exciting and exogenous threshold variables are considered to investigate the impact of a number of market news variables on volatility changes. Adopting a Bayesian approach, we use Markov chain Monte Carlo methods to estimate all unknown parameters and latent variables. A simulation experiment demonstrates good estimation performance for reasonable sample sizes. In a study of two international financial market indices, we consider two variants of the generalized THSV model, with US market news as the threshold variable. Finally, we compare models using Bayesian forecasting in a value‐at‐risk (VaR) study. The results show that our proposed model can generate more accurate VaR forecasts than can standard models.     

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.     

Bayesian analysis of multivariate stochastic volatility with skew return distribution

Multivariate stochastic volatility models with skew distributions are proposed. Exploiting Cholesky stochastic volatility modeling, univariate stochastic volatility processes with leverage effect and generalized hyperbolic skew t-distributions are embedded to multivariate analysis with time-varying correlations. Bayesian modeling allows this approach to provide parsimonious skew structure and to easily scale up for high-dimensional problem. Analyses of daily stock returns are illustrated. Empirical results show that the time-varying correlations and the sparse skew structure contribute to improved prediction performance and Value-at-Risk forecasts.     

Tests for Cointegration Breakdown Over a Short Time Period

We derive a speculative trading model with endogenous informed trading that yields a conditionally heteroscedastic time series for trading volume and the squared price changes. We use half-hourly price-change and volume data for IBM during 1988 to test the model and estimate the structural parameters using the simulated method-of-moments estimation procedure. Although the model seems to do a reasonable job fitting the unconditional moments of the volume and the squared price change processes, it fares less well in fitting the relation between current trading volume and lags of trading volume and squared volume's (and its lag's) relation to squared price changes.     

Testing Target-Zone Models Using Efficient Method of Moments

The objectives of this article are threefold—(1) to test target-zone models using more efficient and direct econometric methodology than previous research, (2) to identify an implicit band, if it exists, from observed data and to test target-zone models based on the estimated implicit band rather than the stated official band, and (3) to examine whether the exchange rate can be modeled as a managed float system with a central parity that lacks a band. We find strong evidence that a model with intramarginal intervention and a narrower implicit (unofficial) band can describe the dynamics of the French franc/Deutsche mark exchange rate from January 1, 1987–July 30, 1993.     

Lag Length Selection for Unit Root Tests in the Presence of Nonstationary Volatility

A number of recent papers have focused on the problem of testing for a unit root in the case where the driving shocks may be unconditionally heteroskedastic. These papers have, however, taken the lag length in the unit root test regression to be a deterministic function of the sample size, rather than data-determined, the latter being standard empirical practice. We investigate the finite sample impact of unconditional heteroskedasticity on conventional data-dependent lag selection methods in augmented Dickey–Fuller type regressions and propose new lag selection criteria which allow for unconditional heteroskedasticity. Standard lag selection methods are shown to have a tendency to over-fit the lag order under heteroskedasticity, resulting in significant power losses in the (wild bootstrap implementation of the) augmented Dickey–Fuller tests under the alternative. The proposed new lag selection criteria are shown to avoid this problem yet deliver unit root tests with almost identical finite sample properties as the corresponding tests based on conventional lag selection when the shocks are homoskedastic.     

LOG-PERIODOGRAM ESTIMATION OF LONG MEMORY VOLATILITY DEPENDENCIES WITH CONDITIONALLY HEAVY TAILED RETURNS

ABSTRACT

Many recent papers have used semiparametric methods, especially the log-periodogram regression, to detect and estimate long memory in the volatility of asset returns. In these papers, the volatility is proxied by measures such as squared, log-squared, and absolute returns. While the evidence for the existence of long memory is strong using any of these measures, the actual long memory parameter estimates can be sensitive to which measure is used. In Monte-Carlo simulations, I find that if the data is conditionally leptokurtic, the log-periodogram regression estimator using squared returns has a large downward bias, which is avoided by using other volatility measures. In United States stock return data, I find that squared returns give much lower estimates of the long memory parameter than the alternative volatility measures, which is consistent with the simulation results. I conclude that researchers should avoid using the squared returns in the semiparametric estimation of long memory volatility dependencies.     

双通道图像／数据传输系统

叙述了基于电视逆程数据插入技术的双通道图像／数据传输系统的设计及关键技术。该系统以电视信号为媒介，无需增加新的信道设备即可将模拟电视通道拓展为模拟／数字兼容通道，可提供传输速率达８６．４ｋｂ／ｓ的高速数据传输。系统性能可靠、传输速率快、纠错能力强，具有广阔的应用前景。     

基于Benders分解算法的跨区互联电力系统协调规划模型

研究跨区互联电力系统的协调规划,对于提高投资效率实现更大范围的资源配置具有较强现实意义。本文首先描述多区域电力系统扩张规划问题,并建立多区域扩张规划模型,旨在寻求最优的扩容方案,以最小投入来满足多区域电力系统负荷增长需求;其次,采用Benders分解算法将多区域扩张规划问题分解为一个规划主问题和一个运行子问题,通过主子问题之间的迭代求解,获得最终的最优解;最后,对某个典型的包含7个区域的多区域电力系统进行模拟仿真,验证了本文所构建模型及算法的有效性。