改善我国失业现状的实现方式

失业是指劳动者在有工作能力并确实在寻找工作的情况下不能得到适宜职业而失去收入的状态.失业是社会生产力发展到一定历史阶段的产物,同时也是经济进一步发展的充分必要条件,是必然的社会现象,与社会制度本身无关.失业是一把"双刃剑",一方面,失业作为劳动力的后备军,可激励在业人员努力工作,提高生产效率;另一方面,失业会造成资源浪费,降低人们生活质量及带来巨大压力,引发系列社会问题甚至稳定.因此,我们应当把失业问题放到战略高度认真加以研究和解决.     

对失业统计的改革思考

我国失业统计起步较晚,在失业的含义、范围和失业统计的方法和指标方面还有待进一步明确和完善.一、明确失业统计的对象失业是指在调查时间内,一定年龄范围内,有劳动能力,没有工作或工作时间没有达到规定标准,正在寻找有报酬工作的人.构成失业的要素有三个:(1)社会、年龄和劳动能力条件.(2)工作时间标准.(3)对有报酬工作的需求.在设计失业统计对象时应紧扣这三要素,并对之加以量化.毫无疑问,公开失业是失业统计的对象.     

论失业统计指标及其界定

失业是困扰全球的社会经济问题,各国政府通常将控制失业规模和降低失业率作为社会经济发展的重要目标。我国目前正处于国民经济体制的转型期,同样面临着严峻的就业形势。本期发表国家统计局程希的文章,论述了在社会主义市场经济条件下,我国失业统计指标及其界定,就新形势下的这一问题进行了探讨。     

失业的监测预警

失业的监测预警中国人民大学顾海兵失业作为一种社会现象，其出现与市场经济有着密切的联系。因为它是自由竞争的产物。在西方市场经济国家，失业始终困扰着各国政府，由失业而引发的国内矛盾和国际矛盾也趋于深刻。因此，当今西方各国政府都把降低失业作为自己的主要目标...     

嘉兴城市失业状况的实证分析

目前，失业现象已成为一个十分重要的社会问题。为了较全面地了解、掌握市区城市劳动力的失业状况，最近，嘉兴市统计局在市级有关部门的配合下在市区城市范围内抽选1000户居民家庭开展城市劳动力失业状况的调查，以期对失业情况作进一步了解，对失业原因进行分析，并提出治理失业的有关对策建议。一、产生失业的原因影响失业的因素很多，既有经济发展模式、结构调整和劳动力供求关系变动等客观方面因素，也有劳动者自身条件方面的主观因素。从经济方面分析，引起失业的原因主要有四个：一是企业改制、转制使隐性失业显性化。根据劳动工资统…     

失业持续期影响因素的地区比较分析

文章利用厦门、长春两市实地调查的样本数据,采用生命表法对两地的失业持续期进行比较,发现失业持续期的长短存在地区差异.应用Cox比例风险回归模型研究影响失业持续期的因素,分析表明失业持续期的影响因素同样存在地区差异.另外地区的经济发展水平、公共就业服务质量、产业结构和就业结构,都会影响当地失业者的失业持续期.     

论建立我国社会承受力指数

当今一些西方国家将通货膨胀率和失业率综合起来,制作所谓的“社会不安指数”或“困难指数”,以反映社会经济的不稳定程度。 在我国通货膨胀与失业同样已成为一个敏感的社会问题,这就从客观上要求建立一个测度通货膨胀与失业的社会承受力的综合指标。因为国家进行宏观管理和决策,需要以社会承受力指数作为参考。通货膨胀和失业对消费者有多大影响?社会可以在何种程度上承受其后果?政府应该心中有数。否则,就不能制订出正确的经济政策;也不能在通货膨胀和失业达到一定程度时,适时地作出反映,以有效地抑制它们持续扩张。     

论失业统计中的问题和解决失业的对策

劳动力失业问题是目前世界各国普遍关心和重视的经济和社会问题之一。我国国情的一个主要特征是人口众多，劳动力资源丰富。而劳动力资源只有通过就业才能创造社会财富，反之就会成为社会的包袱。由于我国自然资源相对贫乏，劳动力失业问题越来越突出。据劳动部提供的统计资料，今年一季度我国失业人数比去年同期增长10．4％，全国城镇登记失业人员达530万人。因此调查和研究劳动力的失业状况，正确搞好失业统计，解决好失业问题，是一项非常重要和紧迫的任务。失业统计反映了劳动力商品在劳动力市场难以实现这一社会经济现象．它的具体指标…     

济宁市劳动力从业结构的政策建议

劳动力就业问题是一个重大的经济问题和社会问题，关系到社会的稳定和人民生活水平的提高。适时地改善我市劳动力的就业结构，就能在一定程度上缓解就业压力，逐步消除因体制性失业、结构性失业、周期性失业对经济社会造成的不利影响。     

我国失业的统计测度探析

政府有关部门的一个重要职责是对失业做出科学的、可操作性的界定,对失业状况进行定期、准确的调查和测算,并及时向决策部门和社会提供失业的基本情况。本文探讨了我国现行失业统计方法,就失业统计中城镇隐性就业、城镇和农业隐性失业的测度等问题进行了深入研究,并从调整改进失业统计口径、完善失业统计调查体系、界定合理失业率、建立失业监测与预警系统等方面提出了完善我国失业统计的对策建议。     

我国季度GDP实时数据预测与评价

传统实证研究中使用的当期特定数据存在滞后信息和噪音信息缺陷,导致模型估计结果存在偏误。应用宏观经济实时数据可以有效的剔除造成模型偏误的滞后信息和噪音信息,得到更为准确的估计结果。MIDAS模型可将低频的关键经济数据与高频数据同时估计,较好的解决了应用一般模型存在的高频数据信息损失问题。本文应用M-MIDAS-DL模型与季度GDP实时数据建立我国季度GDP预测模型,实证表明,应用实时数据与组合预测方法,能及时准确预测出2008年以来中国经济增长率的下滑与反弹走势,能起到较好的提前预警作用,是当前较为有效的经济预测手段之一。     

Term structure forecasting in affine framework with time-varying volatility

This study extends the affine Nelson–Siegel model by introducing the time-varying volatility component in the observation equation of yield curve, modeled as a standard EGARCH process. The model is illustrated in state-space framework and empirically compared to the standard affine and dynamic Nelson–Siegel model in terms of in-sample fit and out-of-sample forecast accuracy. The affine based extended model that accounts for time-varying volatility outpaces the other models for fitting the yield curve and produces relatively more accurate 6- and 12-month ahead forecasts, while the standard affine model comes with more precise forecasts for the very short forecast horizons. The study concludes that the standard and affine Nelson–Siegel models have higher forecasting capability than their counterpart EGARCH based models for the short forecast horizons, i.e., 1 month. The EGARCH based extended models have excellent performance for the medium and longer forecast horizons.     

变权重组合预测模型的局部加权最小二乘解法

随着科学技术的不断进步，预测方法也得到了很大的发展，常见的预测方法就有数十种之多。而组合预测是将不同的预测方法组合起来，综合利用各个方法所提供的信息，其效果往往优于单一的预测方法，故得到了广泛的应用。而基于变系数模型的思想研究了组合预测模型，将变权重的求取转化为变系数模型中系数函数的估计问题，从而可以基于局部加权最小二乘方法求解，利用交叉证实法选取光滑参数。其结果表明所提方法预测精度很高，效果优于其他方法。     

Forecasting interest rates volatilities by GARCH (1,1) and stochastic volatility models

In this paper, we compare the forecast ability of GARCH(1,1) and stochastic volatility models for interest rates. The stochastic volatility is estimated using Markov chain Monte Carlo methods. The comparison is based on daily data from 1994 to 1996 for the ten year swap rates for Deutsch Mark, Japanese Yen, and Pound Sterling. Various forecast horizons are considered. It turns out that forecasts based on stochastic volatility models are in most cases superiour to those obtained by GARCH(1,1) models.     

“丝绸之路经济带”背景下哈萨克斯坦投资风险测度及预警研究

通过建立投资风险突变模型,构建七级投资风险预警表,对哈萨克斯坦国内1995—2014年的投资风险进行测度,研究结果认为:2008年以后哈萨克斯坦投资风险不断上升,其中2008年、2010年、2014年投资风险均达到突变级别。采用GM(1,1)模型和ARMA模型对2015-2017年哈萨克斯坦投资风险指标进行组合预测,并对预测结果进行预警分析,结果显示:2015年、2016年哈萨克斯坦综合投资风险较2014年有所下降,但2017年投资综合风险达到六级预警线以上,存在较大投资风险。     

基于深度学习LSTM神经网络的全球股票指数预测研究

作为深度学习技术的经典模型之一，长短期记忆（LSTM）神经网络在挖掘序列数据长期依赖关系中极具优势。基于深度神经网络优化技术，本文构造了一个深层LSTM神经网络并将其应用于全球30个股票指数三种不同期限的预测研究，结果发现：①LSTM神经网络具有很强的泛化能力，对全部指数不同期限的预测效果均很稳定；②LSTM神经网络具有优秀的预测精度，相比三种对照模型（SVR，MLP和ARIMA），其对全部指数的平均预测精度在不同期限上均有提升；③LSTM神经网络能够有效控制误差波动，其对全部指数的平均预测稳定度相比三种对照模型在不同期限上亦均有提高。鉴于LSTM神经网络在预测精度和稳定度两方面的优势，其未来在金融预测中将有广阔的应用前景。     

Bootstrap forecast intervals for asymmetric volatilities via EGARCH model

Bootstrap forecast intervals are developed for volatilities having asymmetric features, which are accounted for by fitting EGARCH models. A Monte-Carlo simulation compares the proposed forecast intervals with those based on GARCH fittings which ignore asymmetry. The comparison reveals substantial advantage of addressing asymmetry through EGARCH fitting over ignoring it as the conventional GARCH forecast. The EGARCH forecast intervals have empirical coverage probabilities closer to the nominal level and/or have shorter average lengths than the GARCH forecast intervals. The finding is also supported by real dataset analysis of Dow–Jones index and financial times stock exchange (FTSE) 100 index.     

Comparison of Bayesian Model Selection Criteria and Conditional Kolmogorov Test as Applied to Spot Asset Pricing Models

We compare Bayesian and sample theory model specification criteria. For the Bayesian criteria we use the deviance information criterion and the cumulative density of the mean squared errors of forecast. For the sample theory criterion we use the conditional Kolmogorov test. We use Markov chain Monte Carlo methods to obtain the Bayesian criteria and bootstrap sampling to obtain the conditional Kolmogorov test. Two non nested models we consider are the CIR and Vasicek models for spot asset prices. Monte Carlo experiments show that the DIC performs better than the cumulative density of the mean squared errors of forecast and the CKT. According to the DIC and the mean squared errors of forecast, the CIR model explains the daily data on uncollateralized Japanese call rate from January 1, 1990 to April 18, 1996; but according to the CKT, neither the CIR nor Vasicek models explains the daily data.     

Forecasting time series of economic processes by model averaging across data frames of various lengths

This paper presents an extension of mean-squared forecast error (MSFE) model averaging for integrating linear regression models computed on data frames of various lengths. Proposed method is considered to be a preferable alternative to best model selection by various efficiency criteria such as Bayesian information criterion (BIC), Akaike information criterion (AIC), F-statistics and mean-squared error (MSE) as well as to Bayesian model averaging (BMA) and naïve simple forecast average. The method is developed to deal with possibly non-nested models having different number of observations and selects forecast weights by minimizing the unbiased estimator of MSFE. Proposed method also yields forecast confidence intervals with a given significance level what is not possible when applying other model averaging methods. In addition, out-of-sample simulation and empirical testing proves efficiency of such kind of averaging when forecasting economic processes.     

Using large data sets to forecast sectoral employment

We use several models using classical and Bayesian methods to forecast employment for eight sectors of the US economy. In addition to using standard vector-autoregressive and Bayesian vector autoregressive models, we also augment these models to include the information content of 143 additional monthly series in some models. Several approaches exist for incorporating information from a large number of series. We consider two multivariate approaches—extracting common factors (principal components) and Bayesian shrinkage. After extracting the common factors, we use Bayesian factor-augmented vector autoregressive and vector error-correction models, as well as Bayesian shrinkage in a large-scale Bayesian vector autoregressive models. For an in-sample period of January 1972 to December 1989 and an out-of-sample period of January 1990 to March 2010, we compare the forecast performance of the alternative models. More specifically, we perform ex-post and ex-ante out-of-sample forecasts from January 1990 through March 2009 and from April 2009 through March 2010, respectively. We find that factor augmented models, especially error-correction versions, generally prove the best in out-of-sample forecast performance, implying that in addition to macroeconomic variables, incorporating long-run relationships along with short-run dynamics play an important role in forecasting employment. Forecast combination models, however, based on the simple average forecasts of the various models used, outperform the best performing individual models for six of the eight sectoral employment series.