浅谈失业与再就业工程

随着我国经济体制转换和产业结构调整，职工失业和再就业成了社会亟待解决的问题，该文分析了职工失业原因及实施再就业工程的具体措施。     

Unemployment variation over the business cycles: a comparison of forecasting models

Asymmetry has been well documented in the business cycle literature. The asymmetric business cycle suggests that major macroeconomic series, such as a country's unemployment rate, are non‐linear and, therefore, the use of linear models to explain their behaviour and forecast their future values may not be appropriate. Many researchers have focused on providing evidence for the non‐linearity in the unemployment series. Only recently have there been some developments in applying non‐linear models to estimate and forecast unemployment rates. A major concern of non‐linear modelling is the model specification problem; it is very hard to test all possible non‐linear specifications, and to select the most appropriate specification for a particular model. Artificial neural network (ANN) models provide a solution to the difficulty of forecasting unemployment over the asymmetric business cycle. ANN models are non‐linear, do not rely upon the classical regression assumptions, are capable of learning the structure of all kinds of patterns in a data set with a specified degree of accuracy, and can then use this structure to forecast future values of the data. In this paper, we apply two ANN models, a back‐propagation model and a generalized regression neural network model to estimate and forecast post‐war aggregate unemployment rates in the USA, Canada, UK, France and Japan. We compare the out‐of‐sample forecast results obtained by the ANN models with those obtained by several linear and non‐linear times series models currently used in the literature. It is shown that the artificial neural network models are able to forecast the unemployment series as well as, and in some cases better than, the other univariate econometrics time series models in our test. Copyright © 2004 John Wiley & Sons, Ltd.     

基于多元回归分析的中国城镇失业率问题研究

从宏观经济层面分析影响我国城镇失业率的相关因素，采用全序列法对所采集的原始数据进行无量纲化处理。分别应用1978-2009和1990-2007年数据得出多元回归分析只适合对失业率的短期预测。最后根据预测模型得出结论扩大出口，减少进口有利于减少我国城镇登记失业率，多元回归预测只适合对城镇失业率进行短期预测。     

基于BP神经网络的高知识群体失业率预测研究

基于MATLAB仿真平台建立了高知识群体失业率预测的BP神经网络模型,应用数据插值及曲线拟合的方法,补充了高知识群体失业调查中未采集与未公布的数据,解决了失业率预测中的不完全数据问题,训练集和检验集与实际失业数据的仿真实测误差符合实用要求,有效提升了高知识群体失业预测的精确度.     

“高学历失业”生成机制分析

我国"高学历失业"现象与高等学校连续多年扩招有一定关系,但并不是我国高等教育发展过度的结果。"高学历失业"是一种"知识群体"失业,但概括为"知识失业"关不科学,其生成机制是多重的,既有复杂的经济因素,又有深刻的社会因素,是经济因素与社会因素混合交织、综合作用的结果。因此,整治和解决"高学历失业"问题,绝不能抑制教育发展,而必须从经济社会可持续发展的角度,采取各种有效对策进行全方位的综合治理。     

新时期失业弱势群体社会支持政策机制的建构

弱势群体问题已成为我国社会生活中日益凸出的严重社会问题。在弱势群体构成中，失业群体居首位。失业弱势群体的出现，对社会有举足轻重的影响，政府应出台相关政策以支持失业弱势群体。     

光泵磁共振原理的图象阐释

不镣磁共振是利用光抽运效应来研究原子超业细结构塞曼能级间的磁共振，它是近代物理实验中一个基本而重要的实验，本文从以器的相关图象出发，阐释光抽支原理及光磁共振原理，使学生不但能清楚理解光抽运、光磁共振的微观物理过程，且对光泵磁光共振原理达到系统全面的理解。     

我国城镇登记失业率的因素相关性分析与数学模型

本文运用SPSS的相关性分析了城镇登记失业率有关的一些经济指标,通过相关性分析去除了那些与失业率不太相关的指标,得到了影响城镇登记失业率的8个重要指标。接着建立了城镇登记失业率的非线性多元回归数学模型,得到了拟合优度较高达到0.996的模型,并对多元回归模型给出了相关分析,得到对城镇登记就业率影响比较大的是城镇化率,其次分别是第三产业占GDP比重、城镇固定资产投资和市场化程度。     

国内科研不端问题的研究现状及趋势——基于CNKI的文献计量分析

随着国内外科研不端事件的披露,科研不端问题逐渐成为学界的研究热点,通过对20世纪90年代以来CNKI相关数据库中科研不端问题研究成果的增长态势、产出方式、分布结构等指标的统计分析,揭示了我国科研不端问题的研究现状及需要进一步关注的方向。