Feeling at Risk Matters: Water Managers and the Decision to Use Forecasts

Experts contend that weather and climate forecasts could have an important role in risk management strategies for community water systems. Yet, most water managers make minimal use of these forecasts. This research explores the determinants of the use of weather and climate forecasts by surveying managers of community water systems in two eastern American states (South Carolina and the Susquehanna River Basin of Pennsylvania). Assessments of the reliability of weather and climate forecasts are not driving their use as water managers who find forecasts reliable are no more likely to use them than are managers who find them unreliable. Although larger systems and those depending on surface water are more likely to use forecasts for some (but not all) purposes, the strongest determinant of forecast use is risk perceptions. Water managers who expect to face problems from weather events in the next decade are much more likely to use forecasts than are water managers who expect few problems. Their expectations of future problems are closely linked with past experience: water managers who have had problems with specific types of weather events (e.g., flood emergencies) in the last 5 years are likely to expect to experience problems in the next decade. Feeling at risk, regardless of the specific source of that weather-related risk, stimulates a decision to use weather and climate forecasts.     

Are official forecasts of output growth in the EU still biased?

An effective budgetary process requires accurate forecasts of future economic activity. Using data from Stability and Convergence Programmes and the European Commission’s Spring Forecasts, evidence is presented here which indicates deficiencies in official forecasting in the EU arising under the enhanced EU fiscal framework. Forecasts of output growth are unduly pessimistic, irrational, and influenced by recent economic activity. The article considers the reasons for and implications of such biases and proposes improvements to forecasting processes that could address these issues.     

Some Problems in Forecasting Population for Government Planning Purposes

Some governments rely on centralized, official sets of population forecasts for planning capital facilities. But the nature of population forecasting, as well as the milieu of government forecasting in general, can lead to the creation of extrapolative forecasts not well suited to long-range planning. This report discusses these matters, and suggests that custom-made forecasts and the use of forecast guidelines and a review process stressing forecast assumption justification may be a more realistic basis for planning individual facilities than general-purpose, official forecasts.     

异质信念与公司融资工具选择——兼析中国市场股权融资偏好

本文选用沪深两市2006.1-2009.9发布融资公告的上市公司为研究对象,对投资者异质信念与公司融资工具选择的关系进行探讨。研究结果发现,当公司融资工具为股权或直接债券时,投资者异质信念越大,公司越倾向于股权融资。同时还发现,中国市场股权和直接债券融资公告发布前的投资者异质信念差异性显著高于美国市场,这可以由我国市场的投资者结构和卖空机制来解释。     

Bayesian demography 250 years after Bayes

Bayesian statistics offers an alternative to classical (frequentist) statistics. It is distinguished by its use of probability distributions to describe uncertain quantities, which leads to elegant solutions to many difficult statistical problems. Although Bayesian demography, like Bayesian statistics more generally, is around 250?years old, only recently has it begun to flourish. The aim of this paper is to review the achievements of Bayesian demography, address some misconceptions, and make the case for wider use of Bayesian methods in population studies. We focus on three applications: demographic forecasts, limited data, and highly structured or complex models. The key advantages of Bayesian methods are the ability to integrate information from multiple sources and to describe uncertainty coherently. Bayesian methods also allow for including additional (prior) information next to the data sample. As such, Bayesian approaches are complementary to many traditional methods, which can be productively re-expressed in Bayesian terms.     

The Relationship between the Volatility of Returns and the Number of Jumps in Financial Markets

We propose a methodology to employ high frequency financial data to obtain estimates of volatility of log-prices which are not affected by microstructure noise and Lévy jumps. We introduce the “number of jumps” as a variable to explain and predict volatility and show that the number of jumps in SPY prices is an important variable to explain the daily volatility of the SPY log-returns, has more explanatory power than other variables (e.g., high and low, open and close), and has a similar explanatory power to that of the VIX. Finally, the number of jumps is very useful to forecast volatility and contains information that is not impounded in the VIX.     

Incorporating heterogeneous intercourse records into time to pregnancy models

Information on the timing of intercourse relative to ovulation can be incorporated into time to pregnancy models to improve the power to detect covariate effects, to estimate the day-specific conception probabilities, and to distinguish between biological and behavioral effects on fecundability, and therefore the probability of conception in a menstrual cycle. In this paper, Bayesian methods are proposed for joint modeling of intercourse behavior and biologic fecundability. The model accommodates a sterile subpopulation of couples, general covariate effects, and heterogeneity among fecund couples in menstrual cycle viability and in frequency of unprotected intercourse. Methods are described for incorporating cycles with varying amounts of intercourse information into a single analysis. A Markov chain Monte Carlo algorithm is outlined for estimation of the posterior distributions of the unknowns. Themethods are applied to data from a North Carolina study of couples attempting pregnancy.     

改进BP神经网络在经济预测中的应用

针对传统BP学习算法收敛速度慢、对步长依赖明显等缺点，提出一种利用搜索较优步长的BP算法。其在网络训练中，能够在每次迭代中搜索出一个相对合理的步长，从而使步长的选择对学习速度的影响大大降低。对经济预测仿真结果表明，新算法对步长选择的依赖性小于传统BP算法。     

INDEX TO VOLUME 7 (1989)

Surveys of forecasters, containing respondents’ predictions of future values of key macroeconomic variables, receive a lot of attention in the financial press, from investors and from policy makers. They are apparently widely perceived to provide useful information about agents’ expectations. Nonetheless, these survey forecasts suffer from the crucial disadvantage that they are often quite stale, as they are released only infrequently. In this article, we propose MIDAS regression and Kalman filter methods for using asset price data to construct daily forecasts of upcoming survey releases. Our methods also allow us to predict actual outcomes, providing competing forecasts, and allow us to estimate what professional forecasters would predict if they were asked to make a forecast each day, making it possible to measure the effects of events and news announcements on expectations.