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

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.     

Computing maximum likelihood estimates from type II doubly censored exponential data

It is well-known that, under Type II double censoring, the maximum likelihood (ML) estimators of the location and scale parameters, θ and δ, of a twoparameter exponential distribution are linear functions of the order statistics. In contrast, when θ is known, theML estimator of δ does not admit a closed form expression. It is shown, however, that theML estimator of the scale parameter exists and is unique. Moreover, it has good large-sample properties. In addition, sharp lower and upper bounds for this estimator are provided, which can serve as starting points for iterative interpolation methods such as regula falsi. Explicit expressions for the expected Fisher information and Cramér-Rao lower bound are also derived. In the Bayesian context, assuming an inverted gamma prior on δ, the uniqueness, boundedness and asymptotics of the highest posterior density estimator of δ can be deduced in a similar way. Finally, an illustrative example is included.     

中国家庭道德的困境及其应对

中国家庭道德的困境表现为家庭道德评价的失范性,家庭道德选择的矛盾性和家庭道德调控的弱化。针对以上困境,我们应采取的相应对策是:大力宣传家庭道德的意义与价值;建构与时代精神相适应的家庭道德规范;完善家庭道德调控机制,有效地发挥它们的作用。     

话题转换的顺应性研究

根据顺应理论,使用语言的过程是一个进行选择的过程,语言使用者不仅选择语言形式,同时还选择使用策略。话题转换现象是对社会规约和心理世界的一种顺应。话题转换不当,会产生负面影响,妨碍交际的进行。     

Bayesian modelling of catch in a north-west Atlantic fishery

Summary. We model daily catches of fishing boats in the Grand Bank fishing grounds. We use data on catches per species for a number of vessels collected by the European Union in the context of the Northwest Atlantic Fisheries Organization. Many variables can be thought to influence the amount caught: a number of ship characteristics (such as the size of the ship, the fishing technique used and the mesh size of the nets) are obvious candidates, but one can also consider the season or the actual location of the catch. Our database leads to 28 possible regressors (arising from six continuous variables and four categorical variables, whose 22 levels are treated separately), resulting in a set of 177 million possible linear regression models for the log-catch. Zero observations are modelled separately through a probit model. Inference is based on Bayesian model averaging, using a Markov chain Monte Carlo approach. Particular attention is paid to the prediction of catches for single and aggregated ships.     

中国文学中的女性反叛母题的再现——重读《焦仲卿妻并序》、《原野》、《寒夜》、《无处告别》

当下,中国的女性主义发展到了一定的阶段,在很多方面消解男权,甚至有超过男性的趋势。在文学上,女性主义文本很多,作家的女性主义意识也十分明显。在这样的情况下,我们通过几个文本并借鉴原型批评和女性主义批评的方法来研究中国女性反叛的发展脉络。     

社会转型时期大学生的道德困惑及其引导

处于社会转型时期的青年学生道德认识的混乱和冲突、道德人格的分裂及其矛盾、大学生的个性特征与道德困惑及产生困惑的原因很多,要树立正确的社会价值导向和道德观,优化大学生的道德环境,注重大学生道德自我教育能力的培养。     

整合教育:高校思想政治教育的新思路——应对高校“两课”教学的离散趋向

我国高校将“两课”作为实施思想政治教育的主战场目前存在的最大问题是教育教学的某些方面出现离散趋向。本文通过对高校“两课”中出现的离散趋向的分析，认为在思想政治教育教学实践中要实施整合教育，并提出整合教育的具体构想。     

Empirical likelihood confidence intervals for the mean of a population containing many zero values

If a population contains many zero values and the sample size is not very large, the traditional normal approximation‐based confidence intervals for the population mean may have poor coverage probabilities. This problem is substantially reduced by constructing parametric likelihood ratio intervals when an appropriate mixture model can be found. In the context of survey sampling, however, there is a general preference for making minimal assumptions about the population under study. The authors have therefore investigated the coverage properties of nonparametric empirical likelihood confidence intervals for the population mean. They show that under a variety of hypothetical populations, these intervals often outperformed parametric likelihood intervals by having more balanced coverage rates and larger lower bounds. The authors illustrate their methodology using data from the Canadian Labour Force Survey for the year 2000.