Environmental criteria in the public purchases above the EU threshold values by three Nordic countries: 2003 and 2005

Green Public Procurement (GPP) has been considered as an important policy instrument in the context of sustainable consumption and production. The state and progress of GPP has earlier been measured by questionnaires and interviews, both methods being based on the assessment by the purchaser, and questionnaires having low response rates. Recently, a new method was developed, analyzing the existence of environmental criteria in the calls for tenders. However, the studies have dealt neither with the progress in GPP, nor the statistical evidence of differences between countries. Our aim was to analyze more thoroughly whether the differences in the proportions of ‘green’ calls for tenders between the three Nordic countries, Denmark, Finland and Sweden, in 2003 and 2005 were real, and whether there had occurred any progress between the years concerned. The paper also presents the ‘GPP-record’ method, which enables more valid measurement of the environmental soundness of public purchasing. The statistical analyses were done using logit models with country, year and product group as the explanatory factors. It proved to be relevant to take into account the variation that occurred from the random existence of product groups in the samples of calls for tender. There were less environmental criteria in the calls for tenders in Finland than in Denmark and Sweden in 2003, but in 2005 no significant difference between Finland and Denmark was observed. Both Finland and Sweden saw progress in this area between 2003 and 2005.     

基于叙述性偏好法的理财产品选择偏好研究--以湖南地区为例

论文针对客户理财产品选择的偏好进行研究。为深入分析客户的理财行为,采用叙述性偏好法（Stated Preference）进行问卷设计,同时运用多项罗吉特（Multinomial logit）及巢式罗吉特（Nested logit）计量模型建立消费者理财偏好选择模式,并以湖南地区为例,分析该地区客户对于理财商品的偏好,并研究客户群体的理财产品选择行为模式,以期为银行的理财产品开发和监管部门的管理提供一定参考。     

Early warning systems and systemic banking crises in low income countries: A multinomial logit approach

This paper estimates an early warning system (EWS) for predicting systemic banking crises in a sample of low income countries in Sub-Saharan Africa. Since the average duration of crises in this sample of countries is longer than one year, the predictive performance of standard binomial logit models is likely to be hampered by the so-called crisis duration bias. The bias arises from the decision to either treat crisis years after the onset of a crisis as non-crisis years or remove them altogether from the model. To overcome this potential drawback, we propose a multinomial logit approach, which is shown to improve the predictive power of our EWS compared to the binomial logit model. Our results suggest that crisis events in low income countries are associated with low economic growth, drying up of banking system liquidity and widening of foreign exchange net open positions.     

Modeling voter choice to predict the final outcome of two-stage elections

Most election forecasting research to date has been conducted in the context of single-round elections. However, more than 40 countries in the world employ a two-stage process, where actual voting data are available between the first and the second rounds to help politicians understand their position in relation to each other and to voter preferences and to help them predict the final outcome of the election. In this study we take advantage of the theoretical foundation on voter behavior from the political science literature and the recent methodological advances in choice modeling to develop a Nested Logit Factor Model of voter choice which we use to predict the final outcome of two stage elections and gain insights about the underlying political landscape. We apply the proposed model to data from the first stage and predict the final outcome of two stage elections based on the inferences made from the first stage results. We demonstrate how our proposed model can help politicians understand their competitive position immediately after the first round of actual voting and test its predictive accuracy in the run-off election across 11 different state governorship elections.     

Voluntary Carbon Reporting Prediction: A Machine Learning Approach

In this paper we address the impact of the introduction of the National Greenhouse and Energy Reporting scheme on corporate carbon reporting, and subsequently identify factors that influence the level of voluntary carbon reporting. A review of the literature demonstrates a large number of potential factors have been previously deployed to explain voluntary reporting practices; however, the analytical and empirical methods widely used in the literature have limiting statistical assumptions and confine analysis to a small number of explanatory factors. To address this limitation in prior research we apply advanced machine learning methods, such as gradient boosting machines and random forests, to identify predictive variables through analytical means. We compare the performance of machine learning methods with traditional methods such as logistic regression. We find that machine learning methods significantly outperform logistic regression and provide fundamentally different interpretations of the role and influence of different predictive variables on voluntary carbon reporting. While most variables were not statistically significant in the logit results, a number of key proxies for financial performance, corporate governance, and corporate social responsibility have out-of-sample predictive power of the level of voluntary carbon reporting in the machine learning analysis.     

我国高校军民融合科研创新影响因素研究——基于Ordered Logit模型的实证分析

为研究新时代背景下高校军民融合科研创新影响因素,在分析专家访谈和实地调研资料的基础上,通过问卷调查方法收集数据资料,运用Ordered Logit模型对其影响因素进行了实证分析,并研究了各影响因素对现阶段高校军民融合科研创新发展的影响程度。研究表明,军民融合创新发展的国家战略、军产学研协同创新体系、军民融合科研人才支撑、军民融合科研创新的激励政策、军民融合科研成果知识产权保护等因素对我国高校军民融合科研创新发展面临的主要问题有显著影响。     

Payment shock and mortgage performance

The effect of payment shocks on subprime hybrid ARM mortgage prepayment and delinquency is examined. Using loan level data from private label securities, we modeled the effects of payment shocks on mortgage performance. Our study provided interesting empirical results in three main areas: First, we addressed the effect of payment shocks on subsequent mortgage delinquency. Second, we studied how the effect of payment shocks varies and decays over time. Third, we disentangled the impact of payment shocks based on the reason for the shocks: payment shock due to the expiration of a teaser rate (i.e. “teaser shock”) versus the payment shock due to index rate changes at the time of reset (i.e. “market rate shock”).We find that the effect of payment shock on loan performance varies by the delinquency status of the loan at the time of the shock. That is, the payment shock has the most significant effect on “current” loans rather than loans already in delinquency. Also of note, we find that the effect of a payment shock decays only gradually over time. We find that the impact of “teaser shocks” and “market rate shocks” on mortgage performance do not differ substantially, even though teaser shocks may be somewhat more predictable than market rate shocks. This suggests that either subprime ARM borrowers did not fully understand the product and the extent of the shock at the first reset date or that financially strapped borrowers used the product to speculate and were caught by the teaser shock when they were unable to refinance or sell (i.e. “flip”) their properties .The study suggests that any modification plan designed to eliminate potential payment shocks or to otherwise lower payments will be most effective for loans that are currently performing rather than loans that are already in delinquency.     

基于多项Logit选择模型的在线渠道价格促销效应实证研究

在线渠道下,不同品牌的价格折扣促销的差异性会引起在线消费者品牌选择行为变化。同时,在线的价格比较机制也增加了消费者价格敏感度,如果不分离出这一重要的促销效应,那么会产生估计偏差,影响模型的预测能力。本文首先回顾品牌选择的离散模型中价格促销的研究脉络;然后构建在线渠道消费者品牌选择离散Logit模型;接着在对中国某电子商务公司的消费者实际购买可乐的面板数据分析的基础上,研究价格折扣对于该公司消费者品牌选择行为的影响效应,研究发现：（1）如果不引入价格折扣促销因素,将会过高估计价格的影响;（2）价格折扣促销对于消费者网上品牌选择有显著的影响,且影响为正效应。文章最后剖析价格折扣对在线渠道的消费者品牌选择的行为影响机理,并提出未来的研究方向。     

我国商业银行风险预警模型的构建与检验——基于企业风险管理（ERM）的视角

从企业风险管理（ERM）角度利用Logit模型构建商业银行风险预警模型,并使用上市银行2000—2010年的面板数据进行检验,发现所构建的预警模型是可信的。     

Beliefs about energy,a factor in daily ecological mobility?

The central question is to ascertain whether general energy beliefs have any influence on transport mode choice. These beliefs are explored and determined by using one short open question. Based on an alternative approach of free associations instead of a standardised scale of behaviour, this question is included in a quantitative survey and allows us to obtain meaningful words. These exploratory results show first that beliefs about energy can be considered as general representations like a cost, a power or even an environmental issue. These beliefs are included in a logit model of mode choice that also considers more conventional factors representing socio-demographic characteristics, socio-economic aspects, as well as territorial structure and function. The final results demonstrate that energy beliefs influence mode choice if they include an environmental component, which expresses the negative effects of energy. The effects of these beliefs nonetheless are smaller than those of situational constraints.