双星快速定位通信系统中心站实时控制系统

本文阐述了双星快速定位通信系统(国外称RDSS)中心站中心控制计算机系统在系统中的主要功能、计算机通信的格式、该系统的功能物理模型分析以及相应的软硬件的开发。文中着重阐述了用软件工程开发较大型应用工程软件系统的方法和经验(结构化分析和结构化设计方法)。     

Forecasting economic activity with mixed frequency BVARs

Mixed frequency Bayesian vector autoregressions (MF-BVARs) allow forecasters to incorporate large numbers of time series that are observed at different intervals into forecasts of economic activity. This paper benchmarks the performances of MF-BVARs for forecasting U.S. real gross domestic product growth against surveys of professional forecasters and documents the influences of certain specification choices. We find that a medium–large MF-BVAR provides an attractive alternative to surveys at the medium-term forecast horizons that are of interest to central bankers and private sector analysts. Furthermore, we demonstrate that certain specification choices influence its performance strongly, such as model size, prior selection mechanisms, and modeling in levels versus growth rates.     

企业实时财务预警系统构建探析

本文分析了风险、财务危机和财务预警,提出应建立基于信息技术的人机相结合的实时财务预警系统,实现实时、综合的财务预警。并对实时财务预警系统的监控对象和监控层次进行了探讨,建立了实时财务预警系统的结构和工作流程,试图为实时财务预警系统的研究提供参考思路。     

进口冷冻肉类沙门氏菌快速检验方法的研究

〔目的〕建立一种简便、快速、灵敏、准确的沙门氏菌检验方法。〔方法〕以传统标准方法为基础,与实时荧光PCR快速检测技术结合使用。〔结果〕检验进口冷冻禽畜肉样品3177份,检出沙门氏菌105份。〔结论〕该方法的应用使沙门氏菌阴性样品得以快速放行,沙门氏菌阳性样品的鉴定更准确、科学,在进口动物源性食品的检验检疫工作中具有意义。     

实时荧光定量PCR法检测对虾白斑综合征病毒

对虾白斑病由白斑综合征病毒（WSSV）感染引起,所有养殖对虾对该病高度易感,蟹类、小龙虾、淡水虾以及各种龙虾也易感,但发病率和死亡率有很大差别。多种虾类常表现为持续感染或终身隐性感染。隐性感染虾体内的病毒含量极低,需建立一种快速、灵敏、准确的检测方法。本文应用TaqMan探针技术设计了探针和引物,建立了检测WSSV的实时荧光定量PCR方法。对影响PCR反应的主要因素Mg^2＋浓度和退火温度等进行了优化,证明当Mg2＋浓度为3.5～4.0 mmol,退火温度为59～60℃时可获得最佳的扩增和检测效果。     

Real-time decentralized information processing and returns to scale

We use a model of real-time decentralized information processing to understand how constraints on human information processing affect the returns to scale of organizations. We identify three informational (dis)economies of scale: diversification of heterogeneous risks (positive), sharing of information and of costs (positive), and crowding out of recent information due to information processing delay (negative). Because decision rules are endogenous, delay does not inexorably lead to decreasing returns to scale. However, returns are more likely to be decreasing when computation constraints, rather than sampling costs, limit the information upon which decisions are conditioned. The results illustrate how information processing constraints together with the requirement of informational integration cause a breakdown of the replication arguments that have been used to establish nondecreasing technological returns to scale. November 24, 1999; revised version: March 14, 2000     

Keeping track of global trade in real time

This paper builds an innovative composite world trade-cycle index by means of a dynamic factor model for short-term forecasts of world trade growth of both goods and (usually neglected) services. Trade indicators are selected using a multidimensional approach, including Bayesian model averaging techniques, dynamic correlations, and Granger non-causality tests in a linear vector autoregression framework. To overcome real-time forecasting challenges, the dynamic factor model is extended to account for mixed frequencies, to deal with asynchronous data publication, and to include hard and survey data along with leading indicators. Nonlinearities are addressed with a Markov switching model. Pseudo-real-time empirical simulations suggest that: (i) the global trade index is a useful tool for tracking and forecasting world trade in real time; (ii) the model is able to infer global trade cycles very precisely and better than several competing alternatives; and (iii) global trade finance conditions seem to lead the trade cycle, a conclusion that is in line with the theoretical literature.     

Targeting online display ads: Choosing their frequency and spacing

With real-time bidding, advertisers can decide how often (ad frequency) and in what time interval (ad recency) to show an ad to a specific user. Ads shown often and in quick succession might increase brand and ad recognition on the one hand but might annoy users on the other hand. These recognition and annoyance effects, in turn, can have opposing consequences for click-through rates (CTRs). Guided by theory and previous studies, we examine how the frequency (high vs. low) and the recency (small vs. large time intervals) of ad impressions relates to a banner's CTR and whether this is moderated by the campaign type (diverse or not), the brand's advertising expenditure (high vs. low), and the type of industry (selling durable vs. non-durable goods). To do so, we use a large data set containing information on 5.8?bn ad impressions and 1.8?m clicks delivered for 158 different advertisers from 25 industries. We show that higher ad frequency and higher ad recency relate to a lower CTRs, especially when having a less diverse campaign, for brands which are spending more on advertising, and, to some extent, for firms selling durables.     

On the forecasting performance of a small-scale DSGE model

Dynamic stochastic general equilibrium (DSGE) models have recently become standard tools for policy analysis. Nevertheless, their forecasting properties have still barely been explored. In this article, we address this problem by examining the quality of forecasts of the key U.S. economic variables: the three-month Treasury bill yield, the GDP growth rate and GDP price index inflation, from a small-size DSGE model, trivariate vector autoregression (VAR) models and the Philadelphia Fed Survey of Professional Forecasters (SPF). The ex post forecast errors are evaluated on the basis of the data from the period 1994–2006. We apply the Philadelphia Fed “Real-Time Data Set for Macroeconomists” to ensure that the data used in estimating the DSGE and VAR models was comparable to the information available to the SPF.Overall, the results are mixed. When comparing the root mean squared errors for some forecast horizons, it appears that the DSGE model outperforms the other methods in forecasting the GDP growth rate. However, this characteristic turned out to be statistically insignificant. Most of the SPF's forecasts of GDP price index inflation and the short-term interest rate are better than those from the DSGE and VAR models.