A Bias–Reduced Log–Periodogram Regression Estimator for the Long–Memory Parameter

In this paper, we propose a simple bias–reduced log–periodogram regression estimator, ^d_r, of the long–memory parameter, d, that eliminates the first– and higher–order biases of the Geweke and Porter–Hudak (1983) (GPH) estimator. The bias–reduced estimator is the same as the GPH estimator except that one includes frequencies to the power 2k for k=1,…,r, for some positive integer r, as additional regressors in the pseudo–regression model that yields the GPH estimator. The reduction in bias is obtained using assumptions on the spectrum only in a neighborhood of the zero frequency. Following the work of Robinson (1995b) and Hurvich, Deo, and Brodsky (1998), we establish the asymptotic bias, variance, and mean–squared error (MSE) of ^d_r, determine the asymptotic MSE optimal choice of the number of frequencies, m, to include in the regression, and establish the asymptotic normality of ^d_r. These results show that the bias of ^d_r goes to zero at a faster rate than that of the GPH estimator when the normalized spectrum at zero is sufficiently smooth, but that its variance only is increased by a multiplicative constant. We show that the bias–reduced estimator ^d_r attains the optimal rate of convergence for a class of spectral densities that includes those that are smooth of order s≥1 at zero when r≥(s−2)/2 and m is chosen appropriately. For s>2, the GPH estimator does not attain this rate. The proof uses results of Giraitis, Robinson, and Samarov (1997). We specify a data–dependent plug–in method for selecting the number of frequencies m to minimize asymptotic MSE for a given value of r. Some Monte Carlo simulation results for stationary Gaussian ARFIMA (1, d, 1) and (2, d, 0) models show that the bias–reduced estimators perform well relative to the standard log–periodogram regression estimator.     

基于模糊逻辑系统的非线性组合预测方法研究

针对线性组合预测方法的局限性,本文提出了一种基于高斯型模糊逻辑系统的非线性组合预测新方法,并给出了相应的反向传播学习算法确定模糊系统的参数及模糊子集的划分．理论分析和应用实例表明：该方法具有很强的学习与泛化能力,在处理诸如非线性系统中时间序列的组合建模与预测方面都良好的应用价值．     

Time management as a moderator of relations between stressors and employee strain

The purpose of this study was to examine whether time management behaviours moderated relations between stressors (role conflict, role overload, work-family conflict, and family-work conflict) and strain, as measured by scores on the General Health Questionnaire. It was predicted that use of time management behaviours would be negatively associated with strain. These relations, however, were expected to be mediated by feelings of control over time. It was further predicted that the use of time management behaviours would attenuate stressor-strain relations. Data collected from 525 employed men and women indicated, as predicted, that use of time management behaviours was negatively associated with strain. These relations were partially mediated by feelings of control over time. Moderator tests failed to provide support for time management behaviour as a moderator of stressor-strain relations. Implications of findings are discussed and future directions for time management research are suggested.     

Combining forecasts of electricity consumption in China with time-varying weights updated by a high-order Markov chain model

Electricity consumption forecasting has been always playing a vital role in power system management and planning. Inaccurate prediction may cause wastes of scarce energy resource or electricity shortages. However, forecasting electricity consumption has proven to be a challenging task due to various unstable factors. Especially, China is undergoing a period of economic transition, which highlights this difficulty. This paper proposes a time-varying-weight combining method, i.e. High-order Markov chain based Time-varying Weighted Average (HM-TWA) method to predict the monthly electricity consumption in China. HM-TWA first calculates the in-sample time-varying combining weights by quadratic programming for the individual forecasts. Then it predicts the out-of-sample time-varying adaptive weights through extrapolating these in-sample weights using a high-order Markov chain model. Finally, the combined forecasts can be obtained. In addition, to ensure that the sample data have the same properties as the required forecasts, a reasonable multi-step-ahead forecasting scheme is designed for HM-TWA. The out-of-sample forecasting performance evaluation shows that HM-TWA outperforms the component models and traditional combining methods, and its effectiveness is further verified by comparing it with some other existing models.     

Evaluating Probabilistic Forecasts with Bayesian Signal Detection Models

We propose the use of signal detection theory (SDT) to evaluate the performance of both probabilistic forecasting systems and individual forecasters. The main advantage of SDT is that it provides a principled way to distinguish the response from system diagnosticity, which is defined as the ability to distinguish events that occur from those that do not. There are two challenges in applying SDT to probabilistic forecasts. First, the SDT model must handle judged probabilities rather than the conventional binary decisions. Second, the model must be able to operate in the presence of sparse data generated within the context of human forecasting systems. Our approach is to specify a model of how individual forecasts are generated from underlying representations and use Bayesian inference to estimate the underlying latent parameters. Given our estimate of the underlying representations, features of the classic SDT model, such as the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC), follow immediately. We show how our approach allows ROC curves and AUCs to be applied to individuals within a group of forecasters, estimated as a function of time, and extended to measure differences in forecastability across different domains. Among the advantages of this method is that it depends only on the ordinal properties of the probabilistic forecasts. We conclude with a brief discussion of how this approach might facilitate decision making.     

Interdependency among addictive behaviours and time/risk preferences: Discrete choice model analysis of smoking, drinking, and gambling

This paper simultaneously measures the rate of time preference and the coefficient of risk aversion, as well as investigates the interdependencies of four addictive behaviours: smoking, drinking, pachinko (a popular Japanese form of pinball gambling), and horse betting among a sample of the Japanese population. We reach two main conclusions. First, there are significant interdependencies among the four addictive behaviours, in particular between smoking and drinking and between gambling on pachinko and the horses. Second, we conclude that the higher the time preference rate and the lower the risk aversion coefficient becomes, the more likely individuals smoke, drink frequently, and gamble on pachinko and the horses.     

A Comparison of Ambient Casino Sound and Music: Effects on Dissociation and on Perceptions of Elapsed Time While Playing Slot Machines

This research examined the effects of a casino’s auditory character on estimates of elapsed time while gambling. More specifically, this study varied whether the sound heard while gambling was ambient casino sound alone or ambient casino sound accompanied by music. The tempo and volume of both the music and ambient sound were varied to manipulate temporal engagement and introspection. One hundred and sixty (males = 91) individuals played slot machines in groups of 5–8, after which they provided estimates of elapsed time. The findings showed that the typical ambient casino auditive environment, which characterizes the majority of gaming venues, promotes understated estimates of elapsed duration of play. In contrast, when music is introduced into the ambient casino environment, it appears to provide a cue of interval from which players can more accurately reconstruct elapsed duration of play. This is particularly the case when the tempo of the music is slow and the volume is high. Moreover, the confidence with which time estimates are held (as reflected by latency of response) is higher in an auditive environment with music than in an environment that is comprised of ambient casino sounds alone. Implications for casino management are discussed.     

企业投资风险的度量预测方法和防范策略研究

投资风险是投资活动中实际值达不到期望值的程度。利用期望值、标准差和标准差系数的概念 ,可以度量和预测企业投资项目风险的大小。作者从单个投资项目、二个投资项目和多个投资项目的角度给出了投资风险度量和预测的模型。最后指出企业投资风险的十条防范策略和四条具体措施     

单模光纤的极化模式色散及其测量

本文分析了实用单模光纤的极化模式色散及其测量方法。在实用单模光纤中,由于双折射现象导致两个正交极化模式在传榆过程中改变极化方向并产生时延差即色散,从而使光波脉冲展宽,产生误码,因此限制了光纤的通道容量和传输距离。     

The univariate distribution function for a particular bilinear model

The aim of the paper is to find the univariate stationary distribution of a particular bilinear process. In this context, we propose a novel approach to derive the distribution function. It is based on a recursive formula and allows to relax the conditions on the moments of the process. We also show that the derived approximation converges to the true distribution function. The accuracy of the recursive formula is evaluated for finite sample dimensions by a small simulation study.Received: February 2003, Revised: May 2004,