Cross-Validation Study with Confirmatory Factorial Analysis of the Revised Legal Attitudes Questionnaire Scores

The present article establishes the factorial structure of the Legal AttitudesQuestionnaire, one of the most frequently used measurements for the studyof legal authoritarianism, in several Spanish samples consisting of a communitypopulation (N1 = 618 and N2 = 533). In study 1, confirmatory factorial analysesare carried out by LISREL for different models, based on theory and on previousexploratory analyses. In study 2, based on the models selected in study 1, across-validation study is carried out by multigroup analysis. The structure thatappears to be most adequate is the one based on two trait factors (authoritarianismand anti-authoritarianism) and one of method, in which the tendencies of the participants' answers are included     

Spatiotemporal wind forecasting by learning a hierarchically sparse inverse covariance matrix using wind directions

Given the advances in online data acquisition systems, statistical learning models are increasingly used to forecast wind speed. In electricity markets, wind farm production forecasts are needed for the day-ahead, intra-day, and real-time markets. In this work, we use a spatiotemporal model that leverages wind dynamics to forecast wind speed. Using a priori knowledge of the wind direction, we propose a maximum likelihood estimate of the inverse covariance matrix regularized with a hierarchical sparsity-inducing penalty. The resulting inverse covariance estimate not only exhibits the benefits of a sparse estimator, but also enables meaningful sparse structures by considering wind direction. A proximal method is used to solve the underlying optimization problem. The proposed methodology is used to forecast six-hour-ahead wind speeds in 20-minute time intervals for a case study in Texas. We compare our method with a number of other statistical methods. Prediction performance measures and the Diebold–Mariano test show the potential of the proposed method, specifically when reasonably accurate estimates of the wind directions are available.     

U-Convolutional model for spatio-temporal wind speed forecasting

The increasing penetration of intermittent renewable energy in power systems brings operational challenges. One way of supporting them is by enhancing the predictability of renewables through accurate forecasting. Convolutional Neural Networks (Convnets) provide a successful technique for processing space-structured multi-dimensional data. In our work, we propose the U-Convolutional model to predict hourly wind speeds for a single location using spatio-temporal data with multiple explanatory variables as an input. The U-Convolutional model is composed of a U-Net part, which synthesizes input information, and a Convnet part, which maps the synthesized data into a single-site wind prediction. We compare our approach with advanced Convnets, a fully connected neural network, and univariate models. We use time series from the Climate Forecast System Reanalysis as datasets and select temperature and u- and v-components of wind as explanatory variables. The proposed models are evaluated at multiple locations (totaling 181 target series) and multiple forecasting horizons. The results indicate that our proposal is promising for spatio-temporal wind speed prediction, with results that show competitive performance on both time horizons for all datasets.     

The spillover of war in time and space: exploring some open issues

In spite of a voluminous empirical literature, the diffusion of civil war in time and space is still not fully understood and several issues remain open for debate. This article sheds new light on some of these issues. First, we assess the robustness of results to changes in the definition of neighbourhood (spatial dimension) and in the lag structure (temporal dimension). Second, we investigate the factors that determine the strength of civil war diffusion, focusing in particular on (i) the type of war (e.g. its intensity and scope), (ii) the quality of domestic polity, and (iii) interaction opportunities (e.g. ethnic ties and refugee flows). Third, we look at how the occurrence of war in a neighbouring country affects the duration (and not just the onset) of domestic civil war.     

企业设备更新系统分析

本文应用系统工程的思想、理论和方法,对企业设备更新问题进行了系统分析,提出了设备更新理论研究的新思路,为企业设备更新提供了有益的解决方式。     

流通技术经济分析实践中若干偏误的研讨

重点研究了固定资产折旧方法不当和折旧过度过低、企业不合理高负债、企业无形资产流失等问题。     

我国区域金融风险的时空演化及驱动机制——基于经济四部门视角

文章基于我国2005至2016年的分省数据，采用SMR和基尼系数法刻画了区域金融风险的时空演化趋势，并通过构建多样化空间关联模式，运用空间偏微分方法，从经济四部门视角验证了区域金融风险时空演化的驱动机制。研究发现：从我国区域金融风险的时空演化来看，在时间维度上，大部分省份仍存在较高的金融风险，在空间维度上，样本考察期内东部地区和西部地区金融风险地区内差异较大，中部较小，东部地区和中部地区金融风险的地区间差异最大，东部地区和西部地区次之，中部地区和西部地区最小；区域内政府、企业和家庭部门是导致区域金融风险时空演化的主要原因，且区域间的风险外溢效应加大了对区域金融风险的冲击作用。因此，国家防控金融风险要切中要害，从区域金融风险的源头抓起，充分考虑地区之间的空间关联，防止区域内外驱动机制对区域金融风险的放大作用，避免决策偏误。     

On the causes of the changes in income shares: Some reflections in the light the United States experience

The last 30 years have witnessed a dramatic change in the distribution of income, with the wage share falling in all major industrialized countries. Main-stream analyses, including New Keynesian ones, which retain the notion of factor substitution leading to a “factor intensity” inversely related to its rate of return, have encountered some difficulties in the interpretation of this change. Nonmainstream approaches present an advantage in the explanation of the phenomenon, consisting in the fact that they entail no a priori connections between the changes in distribution and the changes factor proportions. Hence if a change in institutions or in the bargaining strength of the parties affects distribution, income shares may vary significantly (i.e., changes in wages need not be accompanied by changes in labor to output ratio in the opposite direction as in mainstream analyses). Yet empirical observation may question also some of the analyses that have been advanced outside the mainstream. The article will explore the ways in which nonmainstream approaches have interpreted the described changes in distribution, and assess them from an analytical viewpoint and with reference to U.S. data. The purpose is that of pointing at some open questions and problems.     

Optimal menu when agents make mistakes

This paper studies how an optimal menu chosen by a social planner depends on whether agents receive imperfect signals about their true tastes (imperfect self-knowledge) or the properties of available alternatives (imperfect information). Under imperfect self-knowledge, it is not optimal to offer fewer alternatives than the number of different tastes present in the population, unless noise is infinite (agents have no clue about their true preferences). As noise increases, the social planner offers menu items that are closer together (more similar). However, under imperfect information, as noise increases, it could be optimal to construct a menu with more distinct alternatives, restrict the number of options, or, for some finite noise, offer a single item.     

新冠疫情管控期间西安市人口分布演变及影响因素探测——基于多源时空大数据视角

新冠疫情的"超常规"管控举措影响了城市人口分布,研究其演变规律有助于疫情管控期间城市人口活动规律认知、聚集趋势判断,以及指导精准化管控分区划分和场所差异化管控。基于百度热力和POI数据,采用人口密度指数、ESDA及地理探测器,分析了西安市在2020年2-4月疫情管控期间的人口分布变化及其影响因素。结果表明:(1)西安市人口密度值及人口密度波动幅度由一环向郊外递减,且人口数量及人口流动性逐渐恢复。与非疫情阶段相比,居民的日常活动周期未发生较大变化,但受复工后企业错峰午休以及居民减少外出影响,活动特征在局部表现不同。(2)西安市高、较高密度区逐渐增加,较低、低密度区持续减少,而随着疫情管控放开及复工复产推进,城市人口分布逐渐由"中心—外围"转变为"中心—外围、开发区延伸"的空间结构。同时,西安市人口分布呈现明显的高、低值聚集以及内热外冷圈层式特征,且随着人口活力的恢复,这种特征越来越显著。在此基础上,进一步总结出防控导向型、防控—就业需求导向型、就业—消费需求导向型、购物—休闲需求导向型四种人口活动模式的特征。(3)西安市7类设施对人口分布的影响强度为:住宅小区>餐饮设施>生活服务>医疗机构>购物服务>办公场所>公园广场,且住宅小区、餐饮设施、生活服务、医疗机构的影响强度逐渐减弱,购物服务、办公场所、公园广场的影响强度逐渐增强,指出疫情管控影响下城市配套设施与人口分布的耦合关系变化是影响西安市人口分布由相对分散变为更加聚集的重要原因。