论当代教育话语模式及其发展趋向

教育话语（discourse）是指社会生活中以教育和说服为目的来传递和交流信息的话语形式,是人社会化过程中的主导话语,是一种以社会期望为准则的权势话语,说服、引导是其表达策略。主要有训导型、宣传型、传授型三种模式。当代教育话语具有民主化、技术化、风格多变的倾向,这体现了当代社会话语与社会变化的交互作用,不仅反映了教育对象的心理诉求得到重视,也体现出社会实践对话语表现形式的调节和控制。     

基于“一般到简单”建模法的入境旅游需求研究

随着国际旅游业的迅速发展,入境旅游需求成为学术界研究的热点领域。本文基于1979-2008年中国4个主要客源国的数据,采用“一般到简单”建模法分析了入境旅游需求的主要影响因素及预测模型的选择。结果表明,旅游者的忠诚行为是影响入境旅游需求的最主要的因素;价格和收入对不同客源国的旅游需求的影响程度不同;各种经济社会及自然因素等不可控因素都大幅度影响国际旅游需求,使中国旅游业严重受挫;旅游需求的预测应基于经济理论和统计检验结果。     

股市预警系统与构成指标简述

中国股市是一个高风险的股市 ,因此有必要运用现代金融计算技术和计量金融理论建立一套股市预警系统。本文在讨论了建立股市预警系统的必要性、可行性的基础上 ,着重讨论了股市预警系统的指标构成原则和框架 ,将近年来在现代金融理论中发展很快的协整理论(CointegrationTheory)、条件异方差理论、市场有效性理论等引入到股市预警系统框架中来     

Modeling interest rate cycles in India

The present study tries to examine the behaviour of various Indian interest rates such as call money rate, and yields on secondary market securities with maturity periods of 15–91 days, 1-year, 5-years and 10-years. In the first stage, the study investigates the determinants of interest rates and finds that although the interest rates depend on some domestic macroeconomic variables such as yield spread and expected exchange rate, they are mainly affected by the movements of international interest rates, although with some lags. The policy variables such as Bank Rate and Federal Funds Rate did not show any significant impact on any of the interest rates. Further, it was found that the interest rates in the very recent period show some cyclical movements similar to that of the developed countries. Future behaviour of interest rates show that the present cycle of each interest rate would peak at different time points. This expected behaviour in domestic interest rates could be due to the integration of the domestic economy with the international money and financial market. This trend may be same in most of the emerging economies of Asia.     

广义人文主义架构中的现代派小说（下）

1985年后的部分现代派作品,已经由对表现手法的借鉴发展到作品内涵上现代意识的传达,从而使中国当代现代派文学具有了一般意义上的现代主义含义.     

日本高等教育考察记

近些年来,受经济社会发展和政府改革的影响,日本高等教育出现了一些新的变化趋势。本文是作者近期考察日本高等教育的专题报告,内容主要涉及近期日本高等教育发展与变革的若干趋势,以及根据考察访问所引发的对日本大学现状与特点的一些看法。     

Prediction theory for autoregressivemoving average processes

This paper reviews statistical prediction theory for autoregressive-moving average processes wing techniques developed in control theory. It demonstrates explicitly the connectioluns between the statistical and control theory literatures. Both the forecasting problem and the Single extraction problem am considered, udng linear least squares methods. Whereas the classical Statistical theory developed by Wiener and Kolmogomv is restricted to stationary stochaotic processes, the recursive techniques known as the Kalman filter are shown to provide a satisfactory treatment of the difference-stationary care and other more general cases. Complete results for non-invertible moving averages are also obtained.     

Spatio-Temporal Modeling of Residential Sales Data

This article focuses on the location, time, and spatio-temporal components associated with suitably aggregated data to improve prediction of individual asset values. Such effects are introduced in the context of hierarchical models, which we find more natural than attempting to model covariance structure. Indeed, our cross-sectional database, a sample of 7,936 transactions for 49 subdivisions over a 10-year period in Baton Rouge, Louisiana, precludes covariance modeling. A wide range of models arises, each fitted using sampling-based methods because likelihood-based fitting may not be possible. Choosing among an array of nonnested models is carried out using a posterior predictive criterion. In addition, one year of data is held out for model validation. A thorough analysis of the data incorporating all of the aforementioned issues is presented.     

Nonlinearity,Breaks, and Long-Range Dependence in Time-Series Models

We study the simultaneous occurrence of long memory and nonlinear effects, such as parameter changes and threshold effects, in time series models and apply our modeling framework to daily realized measures of integrated variance. We develop asymptotic theory for parameter estimation and propose two model-building procedures. The methodology is applied to stocks of the Dow Jones Industrial Average during the period 2000 to 2009. We find strong evidence of nonlinear effects in financial volatility. An out-of-sample analysis shows that modeling these effects can improve forecast performance. Supplementary materials for this article are available online.     

Hidden Markov Mixture Autoregressive Models: Stability and Moments

This article introduces a parsimonious structure for mixture of autoregressive models, where the weighting coefficients are determined through latent random variables, as functions of all past observations. These latent variables follow a Markov model. We propose a dynamic programming algorithm for forecasting, which reduces the volume of calculations. We also derive limiting behavior of unconditional first moment of the process and an appropriate upper bound for the limiting value of the variance. Further more, we show convergence and stability of the second moment. Finally, we illustrate the efficacy of the proposed model by simulation.