金融市场对我国通货膨胀与收入不平等关系的调节作用分析——基于财富门槛视角

笔者通过构建数理模型,从金融市场财富门槛的角度对我国通货膨胀与收入不平等的关系进行了理论分析。结果发现,一方面,在我国金融市场不发达的现实背景下,形成了规避通货膨胀的财富门槛,财富门槛降低了低收入群体规避通货膨胀的效果,扩大了收入差距;另一方面,通货膨胀又在事实上提高了金融市场的财富门槛,进一步加剧了收入不平等状态。利用我国1978年～2009年时间序列数据,进行Johansen协整检验与Granger因果检验,结果验证了金融市场对我国通货膨胀与收入不平等关系的正向影响作用。     

我国虚拟经济与实体经济适度发展平衡区间：基于微分向量场方法

虚拟经济源于实体经济的再分配环节,随经济和社会的进步不断发生深刻的变化,对实体经济发展产生深刻的影响。虚拟经济与实体经济存在协调发展的平衡区间,以科学发展观为指导将二者发展保持在相对的平衡区间内,可以有效防范和降低金融风险。因此,在依据经济动力系统模型的基础上,提出了虚拟经济自增值的数理基础及对应的适度平衡区间。     

环境规制对绿色技术创新的影响——“波特效应”的中国证据

在我国环境规制实现区域污染治理的过程中,会遭遇污染产业转移、投资领域变迁等规避问题。为检验有关规制措施的有效性,本文基于省际面板数据考察了环境规制与绿色技术创新的关系,研究发现,与环境行政处罚和颁布地方性法规相比,收缴排污费和增加环境保护财政支出更加有效,这说明依靠环境规制推动绿色技术创新的关键在于规制工具的选择。总体而言,财、税、费等市场调控类环境规制工具更适合我国国情。中介效应检验发现,收缴排污费和环境保护财政支出倒逼企业提高研发强度,进而推动绿色技术创新,且二者对绿色技术创新的促进作用均呈现由弱变强、由不显著到显著的门槛效应。值得注意的是,严厉的行政处罚阻碍绿色技术创新,且不管规制强度如何,地方性法规对绿色技术创新并无显著影响。因此,为加强环境治理,促进绿色技术创新,需要构建市场化环境规制体系、注重环境规制顶层设计的落地,并适当提高环境规制强度。     

垂直财政不对称与收入不平等——基于转移支付依赖的门槛效应分析

收入分配不平等是现阶段我国社会的主要矛盾之一。本文实证考察了分税制改革以来政府间纵向财政分配体制对居民收入差距的影响。结果显示,垂直财政不对称提高总体上未起到缩小居民收入分配差距的作用。转移支付依赖度较低时,垂直财政不对称提高有助于降低居民收入不平等;而转移支付依赖度超过某个阈值时,垂直财政不对称提高反而加剧居民收入分配不均衡。在一般性转移支付的门槛效应中,垂直财政不对称提高会降低收入不平等;在专项转移支付的门槛效应中,垂直财政不对称对收入不平等的影响呈现“U型”关系。这意味着政府间财政分配关系改革应合理控制转移支付规模,进一步提高一般性转移支付的比重。     

Threshold effects of terms of trade on Latin American growth

This paper investigates nonlinear relationships between terms of trade volatility (totvol) and economic growth in 14 Latin American economies from 1997 to 2014. In the 2000s, Latin American countries experienced accelerated economic growth often attributed to commodity price booms. We split the sample into two regimes based on totvol thresholds determined by bootstrap techniques. Fixed effects, instrumental variable and dynamic panel regressions address endogeneity in trade growth, subject to traditional economic channels such as domestic investment, population growth, exchange rate, government size, and institutions. We find statistically significant thresholds and stronger trade-growth links during the 2000s commodity boom and in larger economies.     

Combining wavelet decomposition with machine learning to forecast gold returns

This paper combines the discrete wavelet transform with support vector regression for forecasting gold-price dynamics. The advantages of this approach are investigated using a relatively small set of economic and financial predictors. I measure model performance by differentiating between a statistically-motivated out-of-sample forecasting exercise and an economically-motivated trading strategy. Disentangling the predictors with respect to their time and frequency domains leads to improved forecasting performance. The results are robust compared to alternative forecasting approaches. My findings on the relative importances of such wavelet decompositions suggest that the influences of short-term and long-term trends are not stable over the full evaluation period.     

A Novel Compact Cat Swarm Optimization Based on Differential Method

ABSTRACT

A compact cat swarm optimization scheme (cCSO) is proposed in this paper, which is designed to solve application domains plagued with limited memory and less-computation power, as a member of cat swarm optimization algorithms (CSO), it composes of two sub-modes, i.e., tracing and seeking modes, so it keeps the same search logic of CSO. On the other hand, cCSO inherits the main feature of compact algorithms, a normal probabilistic model is used to represent the population of solutions instead of processing an actual population, which ensures the cCSO to have the modest memory requirement. The updating vector for the probabilistic model provides a clear moving direction for cats in next step. A cat without historical position and velocity is applied in the algorithm. When the cat is in seeking mode, it employs a differential operator to update the cat’s position, which makes it possible for the cat to have multiple searching directions. Experimental results show that cCSO has pretty performance compared with respect to some population-based testing benchmarks. And it also shows superior performance in convergence rate to some compact optimization algorithms. The case study of gray image segmentation proves that it suits for solving the optimization problem by limited hardware.     

The relation between municipal and government bond yields in an era of unconventional monetary policy

In this paper, we investigate how the 5‐year Swedish municipal bond yield has been related to the corresponding yield on government bonds during the period that the Riksbank has conducted unconventional monetary policy in terms of bond purchases. Using daily Swedish data on bond yields from February 2015 to January 2018, we first conduct an event study to assess the short‐run effects of the Riksbank's bond‐purchase announcements. We then estimate bivariate vector autoregressive models to study the dynamic relationship between the yields. Results from the event study suggest that the accumulated short‐run effect of the Riksbank's announcements was to lower the government bond yield by approximately 40 to 50 basis points and municipal bond yields by 30 to 35 basis points. Our vector autoregressive analysis indicates—in line with the event study—that an unexpected decrease in the government bond yield initially increases the municipal bond‐yield spread. However, after approximately 4 weeks, the effect has been reversed and the municipal bond‐yield spread is lower than it was initially. By conducting this analysis, we contribute to the understanding of the transmission of unconventional monetary policy.     

Spatio-temporal probabilistic forecasting of wind power for multiple farms: A copula-based hybrid model

Accurate probabilistic forecasting of wind power output is critical to maximizing network integration of this clean energy source. There is a large literature on temporal modeling of wind power forecasting, but considerably less work combining spatial dependence into the forecasting framework. Through the careful consideration of the temporal modeling component, complemented by support vector regression of the temporal model residuals, this work demonstrates that a DVINE copula model most accurately represents the residual spatial dependence. Additionally, this work proposes a complete set of validation mechanisms for multi-h-step forecasts that, when considered together, comprehensively evaluate accuracy. The model and validation mechanisms are demonstrated in two case studies, totaling ten wind farms in the Texas electric grid. The proposed method outperforms baseline and competitive models, with an average Continuous Ranked Probability Score of less than 0.15 for individual farms, and an average Energy Score of less than 0.35 for multiple farms, over the 24-hour-ahead horizon. Results show the model’s ability to replicate the power output dynamics through calibrated and sharp predictive densities.     

基于改进Transformer模型的文本摘要生成方法

传统的文本摘要方法，如基于循环神经网络和Encoder-Decoder框架构建的摘要生成模型等，在生成文本摘要时存在并行能力不足或长期依赖的性能缺陷，以及文本摘要生成的准确率和流畅度的问题。对此，提出了一种动态词嵌入摘要生成方法。该方法基于改进的Transformer模型，在文本预处理阶段引入先验知识，将ELMo(Embeddings from Language Models)动态词向量作为训练文本的词表征，结合此词对应当句的文本句向量拼接生成输入文本矩阵，将文本矩阵输入到Encoder生成固定长度的文本向量表达，然后通过Decoder将此向量表达解码生成目标文本摘要。实验采用Rouge值作为摘要的评测指标，与其他方法进行的对比实验结果表明，所提方法所生成的文本摘要的准确率和流畅度更高。