战术面向服务架构概念及相关技术

战术面向服务架构（TSOA）是实现战术云平台服务基础设施的重要支撑概念。由于战术环境的复杂性，目前对TSOA实施过程中面临问题和相关实现技术的总结还较少。为此，开展了针对TSOA的概念以及相关技术的研究。首先，从战术环境下的TSOA架构需求背景出发，介绍了TSOA架构的组成以及适应性特征；然后，通过分析战术环境下设备和网络等多方面存在的限制性因素，明确实施TSOA架构所面临的挑战以及需要解决的关键问题，并对实现过程中涉及的服务发现、消息传输机制、网络状态自适应等相关技术进行归纳；最后，对国内外的相关研究工作进行总结，期望对推动TSOA架构的具体实施提供一定参考。     

关于加快农田水利供给侧改革的几点思考

我国灌溉面积位居世界第一,主要矛盾由总量不足转变为结构性矛盾,深入推进供给侧结构性改革提上议程。针对存在的主要结构性矛盾,研究提出系统解决方案,即调整结构、去除库存、提升品质、降低成本、补齐短板。调整结构重点是严格落实"以水定地",着力以水资源、水生态、水环境承载能力倒逼灌溉面积调整;去除库存重点是依法实施"僵尸"工程降等报废,及时办理注销登记;提升品质重点是大力发展高效节水灌溉,提升灌溉服务质量;降低成本重点是深化国有灌区管理体制改革,创新"最后一公里"管理模式;补齐短板重点是坚持统分结合、重点在统的原则推进农田水利产权改革,深化农业水价综合改革,建立准确补贴机制。     

协调发展战略下地方政府合作的动力机制探析--以深汕特别合作区为例

党的十九大报告首次提出区域协调发展战略,要求各地充分发挥比较优势,加强政府间合作,着力解决区域发展不平衡不充分的问题。深汕合作区正是实施这一战略的典型代表。其中,政府的战略政策导向、经济全球化、区域一体化和非政府组织的发展是合作区得以形成的主要外部动力;区域协调发展、产业结构调整、官员升迁诉求是内部动力。但是,在合作区发展的过程中也存在一定的阻力。如地方保护主义的抬头、利益博弈、市场运作的行政化。因此,为促进地方政府间良性合作,应不断强化合作的正向激励,降低合作的阻力,保证区域府际合作的长久性。     

我国牧区草原生态补偿机制构建研究

［目的］通过构建一套科学、合理的牧区草原生态补偿机制，解决牧区近10年来主要实施的一系列生态治理项目和措施中存在的草原生态补偿标准普遍偏低、政策缺少灵活性、监管体系不够完善、缺乏相应保障机制等问题。［方法］通过对国内有关草原生态补偿机制相关文献分类梳理，基于我国牧区草原生态环境现状，对比分析国内外实践，总结国外成功经验，定性分析补偿机制要素，对草原生态补偿机制进行全面、系统地分析论述。［结果］我国牧区草原生态补偿主体应以各级政府为主； 补偿对象应包括牧区牧民、草原生态环境保护的投资者和建设者以及牧区地方政府； 因保护草原生态环境而导致牧区和牧民的发展权受限由此产生的机会成本作为草原生态补偿标准； 补偿方式以中央财政纵向转移支付为主，以市场补偿为辅的组合模式。保障机制需完善法律法规、公众参与、监督管理、绩效评估等方面建设。［结论］我国牧区草原生态补偿机制的构建须对补偿责任主体、补偿范围、补偿标准、补偿方式及补偿保障机制等各要素要进行整体性、系统性深入分析，才能确保草原生态补偿机制能够满足牧区实际需求，提供有力保障。     

Bidding for Firms with Unknown Characteristics

When a region successfully attracts a firm by offering subsidies, the firm often commits itself to performance targets in terms of employment. In this paper, we interpret these firm‐specific targets as a consequence of incomplete information. We analyze a model of two regions that compete for a firm, assuming that the firm's productivity is ex ante unknown. We show that performance targets often induce overemployment in high‐productivity firms, and that tax credits are often superior to lump‐sum payments. Moreover, when regions differ in wage rates, the low‐wage region wins the bid and has a higher surplus than under complete information. Finally, we show that, under incomplete information, bidding might not lead to efficient firm location.     

森林食品产业减贫的作用机理及效应分析

首先,介绍森林食品产业减贫的作用机理,主要包括解决资源落后与发展滞后的矛盾;促进贫困地区收入增长;注重多方交流互动。其次,将森林食品产业的减贫效应分为社会减贫效应、经济减贫效应、生态减贫效应这3个部分。最后,提出加强基础设施建设与品牌宣传;增加产业联动效应;持续带动地区增收等建议,以期为政府相关部门制定政策提供决策依据。     

Real income convergence and the patterns of financial integration in the EU

We analyse real income convergence among the EU28 countries throughout 1995–2017 and the relationship with the convergence patterns of financial systems. We apply the nonlinear latent factor model of Phillips and Sul (2007, 2009) to real incomes and the IMF financial development indices for financial markets and financial institutions (Svirydzenka, 2016), and identify convergence clubs endogenously. We have several results. First, income disparities narrowed significantly over the last twenty years; yet, the growth convergence process lost momentum triggered by the global financial crisis and countries legacies shaped up asymmetries that have crystallised. Second, countries' financial systems exhibit high fragmentation, especially for financial markets, with the new EU member states at a lower financial development, confirming the existence of a two-tier Europe. Overall, the convergence patterns for real incomes and financial development are strongly correlated. Finally, the financial structure matters and market-oriented economies feature higher long-run growth, indicating the need to implement pan-European policy actions that increase the opportunities of risk diversification, enhance capital raising and channel large-scale financing to firms.     

宏观审慎政策、经济周期与银行风险承担*

本文基于中国227家商业银行2005－2016年非平衡面板数据，实证检验中国宏观审慎政策实施对于银行风险承担的影响。结果表明：宏观审慎政策增强会在一定程度上抑制银行风险承担，而且这种显著的负向关系并不随着银行风险代理变量、经营辐射范围以及是否有外资入股等条件的改变而发生变化。经济周期会对宏观审慎政策的有效性产生非对称性影响，即相比在经济上行时期，在经济下行时期的宏观审慎政策对银行风险承担的抑制作用更强且更为显著。就可能的影响机制而言，本文发现宏观审慎政策通过提高银行盈利能力，从而降低银行风险承担。       

To cluster or not to cluster?: Understanding geographic clustering by restaurant segment

This study tested whether geographic clustering differs by restaurant segment due to the differences in consumers’ hedonic and utilitarian values by using Ripley’s K function and a Tobit model. This study found that higher priced restaurant segments have stronger clusters than lower priced restaurants, which implies that restaurants that focus on hedonic values tend to cluster more than utilitarian focused restaurants. However, the results differ depending on whether or not restaurants are located within a central business district (CBD). For example, quick service restaurants have stronger clusters than casual restaurants outside CBDs. Practical applications may apply to new restaurants that are attempting to open. Up-scale restaurants have the advantage of reducing research costs by locating near similar restaurants. Moreover, casual restaurants do benefit by clustering near existing ones under the condition that demand is not severely hurt by competition, while quick service restaurants benefit by diffusing from each other.     

Data vs. information: Using clustering techniques to enhance stock returns forecasting

This paper explores the use of clustering models of stocks to improve both (a) the prediction of stock prices and (b) the returns of trading algorithms.We cluster stocks using k-means and several alternative distance metrics, using as features quarterly financial ratios, prices and daily returns. Then, for each cluster, we train ARIMA and LSTM forecasting models to predict the daily price of each stock in the cluster. Finally, we employ the clustering-empowered forecasting models to analyze the returns of different trading algorithms.We obtain three key results: (i) LSTM models outperform ARIMA and benchmark models, obtaining positive investment returns in several scenarios; (ii) forecasting is improved by using the additional information provided by the clustering methods, therefore selecting relevant data is an important preprocessing task in the forecasting process; (iii) using information from the whole sample of stocks deteriorates the forecasting ability of LSTM models.These results have been validated using data of 240 companies of the Russell 3000 index spanning 2017 to 2022, training and testing with different subperiods.