Labor market analysis through transformations and robust multivariate models

The work presents a robust approach to labor share analysis. The estimate of labor share presents various complexities related to the nature of the data sets to be analyzed. Typically, labor share is evaluated by using discriminant analysis and linear or generalized linear models, that do not take into account the presence of possible outliers. Moreover, the variables to be considered are often characterized by a high dimensional structure. The proposed approach has the objective of improving the estimation of the model using robust multivariate regression techniques and data transformation.     

基于乡村振兴战略的湖南省乡村治理体系研究

［目的］乡村治理有效作为实施乡村振兴战略的五大战略总要求之一和重要组成部分，对其进行分析有利于完善农村现代化建设以及促进乡村振兴战略的实施。［方法］文章以湖南省为例，采用问卷调查法和Logistic回归模型法对乡村治理体系进行分析，探讨影响区域乡村治理体系的主要因素。［结果］被调查农户的年龄主要集中在30~60岁，年收入主要集中在1万元以上。进而结合Logistic回归模型可知：社区生活组织、村规民约、农村养老服务设施、留守儿童之家、集中性治丧场所以及村务公开等6个变量对乡村治理体系的开展实施正向显著影响，而年龄对乡村治理体系的开展负向影响。［结论］基于乡村振兴战略发展乡村治理体系主要在于创新乡村治理体系，通过分析影响湖南省乡村治理体系的因素，为乡村有效治理提供参考意见，同时充分发挥乡村治理功能实现乡村振兴战略的全面实施。     

Spurious principal components

The principal component regression (PCR) is often used to forecast macroeconomic variables when there are many predictors. In this letter, we argue that it makes sense to pre-whiten the predictors before including these in a PCR. With simulation experiments, we show that without such pre-whitening, spurious principal components can appear and that these can become spuriously significant in a PCR. With an illustration to annual inflation rates for five African countries, we show that non-spurious principal components can be genuinely relevant in empirical forecasting models.     

基于模糊多属性群决策的维护方式选择研究

维护方式选择是维护管理中的重要工作之一,合理的维护方式既能达到保障设备的稳定运行,又能同时兼顾其它各个方面的要求。由于对维护方式的评价涉及多个部门、人员和属性,有些指标只是一个模糊的概念,因而采用模糊多属性群决策的方法对维护方式进行优先抉择。本文结合A公司的设备维护方式选择问题,尝试使用模糊多属性群决策折衷算法求解最佳的维护方式。     

闽侯县闲置农地生态安全评价研究

[目的]根据闽侯县农用地闲置情况,结合当地社会经济及生态环境发展情况,深入分析闽侯县闲置农地的生态安全发展水平。[方法]建立闽侯县闲置农地生态安全评价指标体系,分别从农村垃圾处理点数量、财政支农资金、外部进入农村投资、农业生产总值、种粮补贴、自然灾害、农业技术水平、农村生物的多样性程度、农村家庭收入结构、农村生态补偿金额、农地的整理规模及环保支出占GDP比重共确定12个指标进行深入研究,运用普通最小二乘法对设计的多元线性回归模型进行回归,进一步对闽侯县闲置农地的生态安全水平进行评价。[结果]农村垃圾处理点数量、财政支农资金、种粮补贴、环保支出占GDP比重、生态补偿金额、生物多样性程度对闽侯县闲置农地生态安全水平起到促进作用,呈正相关趋势;自然灾害则与闲置农地生态安全水平呈负相关趋势。[结论]整体看来,闽侯县闲置农地的生态安全水平主要受到当地经济、环境及生态基础三大因素的影响。     

Impact of an urban growth boundary across the entire house price spectrum: The two-stage quantile spatial regression approach

This study provides evidence for the impact of an urban growth boundary (UGB) on house prices. The study employs a two-stage quantile spatial regression method on a dataset that inventories sales transactions of single-family houses within two miles of either side of the eastern boundary of the primary UGB in King County, Washington. The results show that while the UGB decreases house prices across the entire house price spectrum, the impact is uneven; it is most pronounced for houses in the 5th to 8th decile of prices. These findings should encourage policy makers to adopt sub-housing-market-level policy approaches to address UGB and other urban and regional development policies’ potential impacts on house prices.     

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.     

Secrets to design an effective message on Facebook: an application to a touristic destination based on big data analysis

The objective of this research is to identify which are the key variables for designing a message in a social network that can be used by an advertiser to generate Positive/Negative Engagement. The message’s design variables have been classified into four main groups: (i) Message Tools (presence of text, images, video, labels, applications, interactive games and events calendar or others), (ii) Appropriate Message Structure (length and intelligibility), (iii) Informative Cues (links to the brand, orientation towards the product or the brand, topics relevant to the audience, and a remuneration’s promise) and (iv) Persuasive and Emotional Cues (emotional signals, valence, endorsement and influencer mentions). The focus has been a tourist destination: Brand Spain that is advertised through Facebook. A content analysis was carried out and regression analysis with optimal scaling was used on 180 Spain brand’s publications; 57,626 audience reactions to such publications; and 1361 audience comments on the Brand Spain Official Fan Page. According to our results, from the four blocks of predictive variables, only two of them are useful to predict Positive/Negative Engagement: (i) the use of Message Tools (videos), and (ii) the use of informative cues (relevant topics, links on posts, and post’s orientation towards product).     

The empowerment expectation–perception gap: An examination of three alternative models

Previous empowerment research has focused on subordinate perceptions of empowering leadership and its outcomes. Met‐expectations theory suggests that subordinate expectations of leader behaviours are essential in forming their a posteriori evaluations. To address the lack of investigation of individual expectations in the empowerment literature, in this study, we explore how subordinates' empowerment expectations and perceptions combine to influence their job satisfaction and psychological empowerment based on three alternative, theoretically derived met‐expectation models, namely, the disconfirmation model, the ideal‐point model, and the experiences‐only model. The results of a 2‐stage study of 114 respondents indicate that employees are more satisfied with their work when perceived empowerment exceeds expectations. However, both empowerment perceptions and expectations positively contribute to higher psychological empowerment. We then discuss implications and directions for future research.     

An application of two-stage quantile regression to insurance ratemaking

Two-part models based on generalized linear models are widely used in insurance rate-making for predicting the expected loss. This paper explores an alternative method based on quantile regression which provides more information about the loss distribution and can be also used for insurance underwriting. Quantile regression allows estimating the aggregate claim cost quantiles of a policy given a number of covariates. To do so, a first stage is required, which involves fitting a logistic regression to estimate, for every policy, the probability of submitting at least one claim. The proposed methodology is illustrated using a portfolio of car insurance policies. This application shows that the results of the quantile regression are highly dependent on the claim probability estimates. The paper also examines an application of quantile regression to premium safety loading calculation, the so-called Quantile Premium Principle (QPP). We propose a premium calculation based on quantile regression which inherits the good properties of the quantiles. Using the same insurance portfolio data-set, we find that the QPP captures the riskiness of the policies better than the expected value premium principle.