Brexit: Tracking and disentangling the sentiment towards leaving the EU

On 23 June, 2016, the UK held a referendum to decide whether to stay in the European Union or leave. The uncertainty surrounding the outcome of this referendum had major consequences for public policy, investment decisions, and currency markets. We discuss some of the subtleties involved in smoothing and disentangling poll data in light of the problem of tracking the dynamics of the intention to Brexit, and propose a multivariate singular spectrum analysis method that produces trendlines on the unit simplex. The trendline yield via multivariate singular spectrum analysis is shown to resemble that of local polynomial smoothing, and singular spectrum analysis presents the nice feature of disentangling the dynamics directly into components that can be interpreted as changes in public opinion or sampling error. The merits and disadvantages of some different approaches for obtaining smooth trendlines on the unit simplex are contrasted, in terms of both local polynomial smoothing and multivariate singular spectrum analysis.     

Shrinkage Estimation Strategies in Generalised Ridge Regression Models: Low/High-Dimension Regime

In this study, we suggest pretest and shrinkage methods based on the generalised ridge regression estimation that is suitable for both multicollinear and high-dimensional problems. We review and develop theoretical results for some of the shrinkage estimators. The relative performance of the shrinkage estimators to some penalty methods is compared and assessed by both simulation and real-data analysis. We show that the suggested methods can be accounted as good competitors to regularisation techniques, by means of a mean squared error of estimation and prediction error. A thorough comparison of pretest and shrinkage estimators based on the maximum likelihood method to the penalty methods. In this paper, we extend the comparison outlined in his work using the least squares method for the generalised ridge regression.     

A Geometrical Interpretation of Collinearity: A Natural Way to Justify Ridge Regression and Its Anomalies

Justifying ridge regression from a geometrical perspective is one of the main contributions of this paper. To the best of our knowledge, this question has not been treated previously. This paper shows that ridge regression is a particular case of raising procedures that provide greater flexibility by transforming the matrix X associated with the model. Thus, raising procedures, based on a geometrical idea of the vectorial space associated with the columns of matrix X , lead naturally to ridge regression and justify the presence of the well-known constant k on the main diagonal of matrix X ^′ X . This paper also analyses and compares different alternatives to raising with respect to collinearity mitigation. The results are illustrated with an empirical application.     

Reluctant Generalised Additive Modelling

Sparse generalised additive models (GAMs) are an extension of sparse generalised linear models that allow a model's prediction to vary non-linearly with an input variable. This enables the data analyst build more accurate models, especially when the linearity assumption is known to be a poor approximation of reality. Motivated by reluctant interaction modelling, we propose a multi-stage algorithm, called reluctant generalised additive modelling (RGAM), that can fit sparse GAMs at scale. It is guided by the principle that, if all else is equal, one should prefer a linear feature over a non-linear feature. Unlike existing methods for sparse GAMs, RGAM can be extended easily to binary, count and survival data. We demonstrate the method's effectiveness on real and simulated examples.     

Disentangling the drivers of renewable energy investments: The role of behavioral factors

The scope of this study is to investigate the main determinants of renewable energy investments in Greece. For this reason, we build an appropriate framework to infer the decision‐making process in the renewable energy sources (RES). The main drivers are then categorized under two groups as economic, and behavioral ones. In the next stage, we rely on binomial (logit and probit) and quantile regression analysis to estimate the impact of these factors on investment decisions towards RES. Our findings indicate that investors who gain better access to knowledge and exhibit a more positive attitude towards the contribution of artificial intelligence (AI) on the RES industry have a higher share of renewables in their portfolio. We argue that these investors are willing to invest a higher amount in the RES industry today. At the same time, quantile regression models illustrate that this relationship is nonmonotonic (i.e., inverted U shaped curve), arguing that positive attitudes towards the contribution of behavioral factors on RES are not adequate to encourage investments after crossing a certain point (threshold). Based on the empirical findings, we argue that the message to policymakers and government officials is to promote a faster penetration of low carbon technologies to achieve sustainable economic and social growth.     

地方政府债务对工业资本配置效率的非线性影响效应——基于动态面板平滑转换回归PSTR模型的检验

经济新常态下,提高工业资本等要素配置效率是实现经济向高质量增长转变的必然要求。本文在政府主导、投资驱动的工业发展模式下,结合地方债务压力和金融发展差异门限变量,在非线性框架下运用动态面板平滑转换回归模型实证检验地方债务规模与工业资本配置效率的渐进演变关系。研究发现,适度的地方债务规模有助于提高资本配置效率,但随着债务压力增加,举债对资本配置的正效应逐步减弱并产生负效应。究其原因,适度举债能补齐工业基础设施短板,压低土地成本,对工业发展产生杠杆效应,从而提高资本配置效率;但过度举债推升财政风险,占用信贷资源并强化企业融资约束,造成资本配置低效率。此外,研究发现,地方信贷规模提升,能缓解举债的融资约束,促进工业行业间的资本流动;而工业金融深化程度提高,能增强市场竞争机制在要素配置中的作用,缓解举债造成投资错配,从而增强资本配置效率。以上分析结果表明,地方政府举债应更理性、适度、规范。     

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.     

空间分析在住宅价值评估中应用研究综述

住宅是一种异质性商品，它的构成使用价值的各个特征之间有明显的差异，因此特征价格定价模型广泛用于住宅的价值评估。但是由于住宅数据中常常表现出空间问题，使得传统的特征价格模型不再适用，空间效应在不动产领域的存在引起了学者们的广泛关注。对空间分析在住宅价值评估中的应用研究进行了回顾和阐述，首先分析空间效应的来源和影响，讨论了在传统模型中纳入空间效应的重要性，接着阐述如何对这些空间效应进行有效建模和检验，并分析了进一步研究的方向。     

An empirical analysis of multiple services and choices of consumer in the Swedish telecommunications market

Received analyses state that firms can use a multiple services offering strategy to retain customers and capture new customers. Factors that determine the multiple services strategy include product discount, service provider and customer characteristics. Consequently, this study addresses the fundamental question: what are the key determining factors that explain the probability that a consumer buys multiple services? A generalized Poisson regression model is employed to examine whether the product discount, service provider, socio-economic variables and geographical location impact consumer decisions. Data from a national survey in 2009 commissioned by Post-och Telestyrelsen, the Swedish telecommunications regulator, are analyzed. The results clearly show that the discount, service provider and income of the consumer affect the consumer׳s buying decision. A consumer who receives a discount or has a high income is more likely to buy multiple services or select more services from the current service provider into his basket than a consumer who does not receive discount or has lower income. Service providers, cable TV operators and telecommunications carriers can also lock-in their consumer and expand their market position from one particular service to another using bundling service. Thus, this may be the time for the telecommunications regulator to consider the market definition.     

长三角地区经济增长与环境污染再研究——基于分位数回归方法

运用刻画不同区段边际变化影响的分位数回归方法,对长三角地区经济增长与废水排放关系进行实证检验,得出以下结论:1)长三角地区废水染排放与经济发展呈现同步递增关系,环境库兹涅茨倒U关系在长三角地区并不成立;2)分位数回归分析表明,高收入水平对应相对低的边际排放,随着收入增加长三角地区废水排放增速放缓;3)产业结构、城市化、技术水平等对废水排放的影响随分位点增加呈现递减趋向。