Forecasting and nowcasting emerging market GDP growth rates: The role of latent global economic policy uncertainty and macroeconomic data surprise factors

In this paper, we assess the predictive content of latent economic policy uncertainty and data surprise factors for forecasting and nowcasting gross domestic product (GDP) using factor-type econometric models. Our analysis focuses on five emerging market economies: Brazil, Indonesia, Mexico, South Africa, and Turkey; and we carry out a forecasting horse race in which predictions from various different models are compared. These models may (or may not) contain latent uncertainty and surprise factors constructed using both local and global economic datasets. The set of models that we examine in our experiments includes both simple benchmark linear econometric models as well as dynamic factor models that are estimated using a variety of frequentist and Bayesian data shrinkage methods based on the least absolute shrinkage operator (LASSO). We find that the inclusion of our new uncertainty and surprise factors leads to superior predictions of GDP growth, particularly when these latent factors are constructed using Bayesian variants of the LASSO. Overall, our findings point to the importance of spillover effects from global uncertainty and data surprises, when predicting GDP growth in emerging market economies.     

Shift-contagion in energy markets and global crisis

This paper presents an analysis of shift-contagion in energy markets, testing whether linkages between returns in energy markets increase during crisis periods. The research presented herein demonstrates how common movement between energy markets increases due to (i) shift-contagion across energy markets, reflected by structural transmission of shocks across markets and (ii) larger common shocks operating through standard cross-market interdependences. A regime-switching model was developed to detect shift-contagion across energy markets. In the approach adopted herein, the occurrence of shift-contagion is endogenously estimated rather than being exogenously assigned. The results show that shift-contagion has been a major feature of energy markets over the last decade. Evidence is presented which demonstrates that the linkages between energy markets do not appear to be stable. These results are remarkably accurate for forecasting Brent and natural gas for horizons for up to 50 days. Conversely, for WTI (West Texas Intermediate oil) and coal, the model performs well only for forecasting very short horizons (up to 20 days). For all products, the model shows significant biases for long horizons.     

Identifying US business cycle regimes using dynamic factors and neural network models

We use dynamic factors and neural network models to identify current and past states (instead of future) of the US business cycle. In the first step, we reduce noise in data by using a moving average filter. Dynamic factors are then extracted from a large-scale data set consisted of more than 100 variables. In the last step, these dynamic factors are fed into the neural network model for predicting business cycle regimes. We show that our proposed method follows US business cycle regimes quite accurately in-sample and out-of-sample without taking account of the historical data availability. Our results also indicate that noise reduction is an important step for business cycle prediction. Furthermore, using pseudo real time and vintage data, we show that our neural network model identifies turning points quite accurately and very quickly in real time.     

因子von Neumann代数上的非线性中心化子

设m，n是任意非零整数，且满足(m＋n)(m－n)≠0， M是实或复数域F上的Hilbert空间上的一个因子von Neumann代数.利用代数分解方法证明了M上满足2mφ(AB)＋2nφ(BA)＝mφ(A)B＋mAφ(B)＋nφ(B)A＋nBφ(A)的非线性映射φ为可加中心化子，并刻画出具体形式φ：A→λA(λ∈F， A∈M).     

国际石油价格波动的结构性因素分析

文章将石油供给性冲击、需求性冲击和投机行为冲击3方面结构性因素细分为5个内生变量,构建反映油价波动的结构向量自回归(SVAR)模型,并运用模型对1999—2012年期间油价波动的原因进行了实证分析。结果表明:需求性冲击无论是长期还是短期都是油价波动的最主要因素;其次,投机性冲击对油价波动的影响也较大,不容忽视;短期突发事件和供给冲击短期对油价有些影响,长期影响基本消失。     

基于模糊证据推理的突发事件风险分析模型

突发事件发生、发展和演化过程中的风险属性来源复杂,且相关信息具有不确定、不完备的特点.为准确分析这一特点下的突发事件运行风险问题,引入模糊证据推理构建分析模型.首先根据突发事件的结构化描述框架和故障模式影响危害分析(FMECA)方法构建突发事件风险属性识别框架,获取信息作为风险分析证据并对其进行规范化处理;然后以模糊证据推理构建突发事件风险分析的模型,通过计算,给出事件风险程度的合理判断;最后选取CRH某型动车组行车安全事件进行实例分析.分析结果表明,在分析突发事件风险时,针对信息不确定、不完备特点构建的模糊证据推理模型具有可行性与有效性,同时该模型能够为突发事件风险分析和风险应对决策的制定提供辅助参考.     

水平地震作用下建筑结构扭转规则性研究进展

我国是一个地震频发的国家,在水平地震作用下,扭转作用对不规则结构具有显著影响。针对4次著名的大地震,对不规则结构的扭转不利影响做了分析;对国内外有关结构规则性的研究进展、国内专家学者在结构扭转规则性问题上的学术碰撞做了全面的总结;对规范扭转指标—位移比和周期比的理论基础及其研究进展做了总结和研讨,指出了在结构动力学基础上所得到的结论与位移比指标、周期比指标之间存在的逻辑瑕疵,并提出了需要进一步研究的方向与建议。     

人工智能促进服务业生产率了吗

近年来，我国服务业占比呈现持续上涨态势，但服务业的高成本低效率问题仍然存在，随着人工智能和服务业的融合，服务业的全要素生产率是否提高有待进一步验证。我们运用DEA-Malmquist生产率指数验证了“鲍莫尔病”的确存在；加入人工智能因素后的服务业全要素生产率的确提高了，但是进一步的分解发现，全要素生产率的提高主要源于要素配置效率的改善和规模报酬递增效应，并非由于技术进步，人工智能作为技术进步的主要标志在提高服务业全要素生产率中的作用仍然有限，这是由于人工智能作用的滞后性所致。     

中国功能农业发展现状、问题与策略

 发展功能农业，强化中国农产品的营养功能，尤其是提升农产品微量营养素的有效供给，解决“隐性饥饿”，已经成为现代农业创新发展的重要战略方向。分析了中国功能农业发展的背景意义、现状问题，提出了中国功能农业发展的三原则、重点强化的微量营养素、主要农业载体、科技创新体系以及重点行动计划等建议与对策，认为微量营养生物强化技术及策略更适合中国国情，有利于中国现代农业的转型升级、提质增效、农业供给侧结构性改革和新动能培育。     

Flow and heat transfer behaviour of nanofluids in microchannels

Flow and heat transfer of aqueous based silica and alumina nanofluids in microchannels were experimentally investigated. The measured friction factors were higher than conventional model predictions at low Reynolds numbers particularly with high nanoparticle concentrations. A decrease in the friction factor was observed with increasing Reynolds number, possibly due to the augmentation of nanoparticle aggregate shape arising from fluid shear and alteration of local nanoparticle concentration and nanofluid viscosity. Augmentation of the silica nanoparticle morphology by fluid shear may also have affected the friction factor due to possible formation of a core/shell structure of the particles. Measured thermal conductivities of the silica nanofluids were in approximate agreement with the Maxwell-Crosser model, whereas the alumina nanofluids only showed slight enhancements. Enhanced convective heat transfer was observed for both nanofluids, relative to their base fluids (water), at low particle concentrations. Heat transfer enhancement increased with increasing Reynolds number and microchannel hydraulic diameter. However, the majority of experiments showed a larger increase in pumping power requirements relative to heat transfer enhancements, which may hinder the industrial uptake of the nanofluids, particularly in confined environments, such as Micro Electro-Mechanical Systems (MEMS).