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.     

Forecasting of electricity price through a functional prediction of sale and purchase curves

This work proposes a new approach for the prediction of the electricity price based on forecasting aggregated purchase and sale curves. The basic idea is to model the hourly purchase and the sale curves, to predict them and to find the intersection of the predicted curves in order to obtain the predicted equilibrium market price and volume. Modeling and forecasting of purchase and sale curves is performed by means of functional data analysis methods. More specifically, parametric (FAR) and nonparametric (NPFAR) functional autoregressive models are considered and compared to some benchmarks. An appealing feature of the functional approach is that, unlike other methods, it provides insights into the sale and purchase mechanism connected with the price and demand formation process and can therefore be used for the optimization of bidding strategies. An application to the Italian electricity market (IPEX) is also provided, showing that NPFAR models lead to a statistically significant improvement in the forecasting accuracy.     

Forecasting of dependence,market, and investment risks of a global index portfolio

This paper undertakes an in-sample and rolling-window comparative analysis of dependence, market, and portfolio investment risks on a 10-year global index portfolio of developed, emerging, and commodity markets. We draw our empirical results by fitting vine copulas (e.g., r-vines, c-vines, d-vines), IGARCH(1,1) RiskMetrics value-at-risk (VaR), and portfolio optimization methods based on risk measures such as the variance, conditional value-at-risk, conditional drawdown-at-risk, minimizing regret (Minimax), and mean absolute deviation. The empirical results indicate that all international indices tend to correlate strongly in the negative tail of the return distribution; however, emerging markets, relative to developed and commodity markets, exhibit greater dependence, market, and portfolio investment risks. The portfolio optimization shows a clear preference towards the gold commodity for investment, while Japan and Canada are found to have the highest and lowest market risk, respectively. The vine copula analysis identifies symmetry in the dependence dynamics of the global index portfolio modeled. Large VaR diversification benefits are produced at the 95% and 99% confidence levels by the modeled international index portfolio. The empirical results may appeal to international portfolio investors and risk managers for advanced portfolio management, hedging, and risk forecasting.     

中国与“一带一路”沿线部分国家的经济关系动态分析

GTAP(Global Trade Analysis Project)模型综合考虑了资本总量、资本流动、人口因素、国民生产总值等多项指标,可以用于各相关国的经济发展和经济合作互动关系分析.选用GTAP模型,分析了中国与一带一路沿线部分国家的经济互动关系.在西亚地区选择了沙特阿拉伯、以色列、伊朗、阿曼、阿联酋、科威特、伊拉克共7国,分别在降低关税20%和降低关税50%两种假设条件下展开实证分析.实证结果显示:在关税分别降低20%和50%两种假设条件下,中国和西亚诸国的GDP都有一定程度增加,各国之间进出口规模都有一定程度的扩大,这表明"一带一路"倡议对于沿线各国的经济发展和合作互动具有明显的促进作用.     

基于多模态融合技术的用户画像方法

针对当前用户画像工作中各模态信息不能被充分利用的问题, 提出一种跨模态学习思想, 设计一种基于多模态融合的用户画像模型。首先利用 Stacking集成方法, 融合多种跨模态学习联合表示网络, 对相应的模型组合进行学习, 然后引入注意力机制, 使得模型能够学习不同模态的表示对预测结果的贡献差异性。改进后的模型具有精心设计的网络结构和目标函数, 能够生成一个由特征级融合和决策级融合组成的联合特征表示, 从而可以合并不同模态的相关特征。在真实数据集上的实验结果表明, 所提模型优于当前最好的基线方法。     

伊陕斜坡东南部延长组页岩气地球化学特征分析

鄂尔多斯盆地陆相页岩气勘探潜力巨大,但对于陆相页岩气成因类型的研究相对滞后,限制了对页岩气的进一步勘探和开发。通过综合化学组分分析和碳、氢同位素分析的手段对伊陕斜坡东南部延长组页岩气和原油伴生气的地球化学特征及成因进行研究。研究结果表明延长组页岩气(生产气和真空解吸气)和原油伴生气都以烷烃类气体为主,其中甲烷含量都小于95%,非烃气体含量比较低。页岩解吸气中,非烃气体比例相对较高,且氧气含量异常高,这与解吸装置密封性不好或者装置本身残留空气清除不彻底有关。页岩气和原油伴生气的甲烷含量低、干燥系数(C_1/C_(1~5))主要集中在0.6~0.9之间、C_2/C_3都小于3,δ~(13)C_1值分布于-52.0‰~-44.9‰之间、δ~(13)C_2值都小于-29‰,δ~(13)C_3值都小于-25.5‰和δD_1都小于-150‰,指示研究区页岩气和原油伴生气以陆相环境热成因的热解湿气(油型气)为主。页岩气和原油伴生气样品有相对高的正庚烷含量和正构烷烃(nC_(5-7))含量,δ~(13)C_2值分布于-41.1‰~-31.1‰之间,说明延长组页岩气与原油伴生气都属于偏腐泥型天然气。此外,延长组页岩气和原油伴生气碳同位素系列基本都属正碳同位素系列,且δ~(13)C_1与δ~(13)C_2值,δ~(13)C_2值与δ~(13)C_3值有较好的正相关关系,这也表明页岩气和原油伴生气具有相同或相似的母质来源。     

基于炉灶热量回收的吸热装置设计

设计了一种基于炉灶热量回收的吸热装置。该装置通过将导热性能及焊接性能优良的无氧紫铜管与炉灶支架焊接成一个整体,近距离吸收炉灶火焰四周散失的热量,利用火焰散失的热量给生活用水加热,将生成的热水供日常生活使用;对出水装置进行改良,使其与加热装置形成循环结构,达到对整个系统进行保护的目的。本装置提高了对能源的利用率,减少了能源消耗,达到节能的目的。     

新型工业化与新型城镇化时空耦合协调性分析——以我国西部12省为例

基于新型工业化和新型城镇化的内涵,通过建立复合系统协调度模型,从静态和动态的角度对我国西部12省区新型工业化与新型城镇化的时空耦合协调性进行分析评价。结果显示:西部地区新型工业化和新型城镇化交互作用,共同推进社会经济系统有序向前发展,新型工业化与新型城镇化的协调发展过程具有空间分异性和动态波动性。进而提出促进我国西部地区新型工业化与新型城镇化协调发展的对策建议。