Directed graphs,information structure and forecast combinations: an empirical examination of US unemployment rates

Previous studies show that it is not always optimal to combine forecasts of alternative models. In this paper, we propose to use the recent advances in modeling directed acyclic graphs to study the issue of forecast combinations. In forecasting US unemployment rates, we demonstrate that the proposed procedure can be a useful tool for comparing information in rival forecasts and guiding the combination of individual forecasts. Copyright © 2009 John Wiley & Sons, Ltd     

中国经济转型时期的失业现象及出路

随着我国经济体制改革的不断深化，建立现代企业制度步伐的加快，失业现象日益突出，出现了各种类型的失业人口，我国在未来一个时期的就业形势不容乐观。要比较好的解决这个问题，就要找出造成失业的原因，并做好以下工作：保持国民经济的持续增长；大力发展第三产业；调整所有制结构，扶持非公有制经济；提高劳动者自身的素质，转变就业观念；培育劳动力市场；建立和健全社会保障体系。     

运用"时空定位系统指导法"指导困难就业群体就业的设想

为适应就业市场新机制的要求,强化对困难就业群体的职业指导,提出“时空定位系统指导法”。时空定位系统指导法由融为一体的时空思维法、定位分析法和系统调适法组成。时空思维法是认知困难就业群体的理性思维方法,定位分析法是分析困难就业群体的技术应用方法,系统调适法是指导困难就业群体的对症治疗方法。     

中国失业警戒线的研究和分析

失业是市场经济发展的必然产物，在许多国家。失业率居高不下已引发出许多政治、经济、社会等问题，所以研究失业率、建立失业警戒线已成为世界各国政府普遍关注的问题。阐明了失业警戒线的确定应包括两个方面。即警戒线本身的确定和警戒区间分界点的确定。在当前，宜采取定性方法采确定失业警戒线。而通过建立教学模型进行模拟的方法将是未来研究的方向。     

生产资源配置优化模型在估计我国农业隐性失业量中的应用

当前我国农村存在大量的隐性失业,这给我国经济发展和社会进步带来了不少问题。采用Cobb-Douglas生产函数的生产资源配置优化模型对我国农村的隐性失业量进行估计,分析我国近年来农业隐性失业率居高不下的原因,并提出解决这一问题的相关政策建议。     

Short-term forecasting of the US unemployment rate

This paper aims to assess whether Google search data are useful when predicting the US unemployment rate among other more traditional predictor variables. A weekly Google index is derived from the keyword “unemployment” and is used in diffusion index variants along with the weekly number of initial claims and monthly estimated latent factors. The unemployment rate forecasts are generated using MIDAS regression models that take into account the actual frequencies of the predictor variables. The forecasts are made in real time, and the forecasts of the best forecasting models exceed, for the most part, the root mean squared forecast error of two benchmarks. However, as the forecasting horizon increases, the forecasting performance of the best diffusion index variants decreases over time, which suggests that the forecasting methods proposed in this paper are most useful in the short term.     

“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。     

广州市城市失业特征及其治理

论述了了广州市城市失业的特征,分析了在诸多的优惠就业政策下广州市的城市失业问题仍然得不到很好解决的原因,提出了一些政策建议。     

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.