近十年陕北耕地资源变化与粮食安全分析

该文以包括榆林市和延安市的陕北地区为研究对象,对陕北地区1996～2006年耕地动态变化与粮食生产情况进行分析研究。选择最小人均耕地面积和压力指数模型法,在分析陕北地区1996～2006年间耕地、人口、粮食动态变化的基础上,总结了该区域最小人均耕地面积和耕地压力的变化特点:陕北地区最小人均耕地面积的平均值是0.219hm2,耕地压力指数的平均值K是1.389。K值远大于1,因此耕地压力较大。在以上基础上提出了保障陕北地区耕地动态平衡与粮食安全生产的一些建议。     

Solvency Ⅱ与C-ROSS非寿险保险风险最低资本要求比较研究——基于中国60家财产保险公司的经验证据

本文以非寿险业务保险风险最低资本要求为考察对象,研究了欧盟Solvency Ⅱ与中国C-ROSS的差异,并利用中国保险市场60家财险公司的经验数据,对两者之间的差异进行了实证和模拟分析。研究结果表明,Solvency Ⅱ和C-ROSS对中国财险公司保险风险最低资本要求存在差异。对于拥有传统业务结构的财险公司,Solvency Ⅱ对保险风险最低资本要求更高,但是这种差距随着公司业务规模的缩小而减弱;对于以经营某些专业险种为主的财险公司,主营业务险种对两者差异具有决定性影响。本文的研究结论详细解释了Solvency Ⅱ与C-ROSS在非寿险保险风险最低资本计算上的异同,对C-ROSS下一步的修订工作提供了一定的支持与参考。     

The effect of education on the minimum wage

This research shows for the first time that the level of education has a causal, negative effect on the minimum wage. I use 2SLS, with historical educational data as an instrument for the level of education in 2010, and I find that across the US states a one percentage point greater proportion of college graduates is associated with a real minimum wage that is lower by 1.5%–1.6%. Also, in order to control for state-level omitted variables, I regress the change in the minimum wage on the change in education and I find again a negative, and significantly at the 1% level, effect. Minimum wage is a policy that is chosen by governments according to voters’ preferences. The results of this research imply that when the level of education increases voters prefer a lower minimum wage.     

Multivariate Hill Estimators

We propose two classes of semi‐parametric estimators for the tail index of a regular varying elliptical random vector. The first one is based on the distance between a tail probability contour and the observations outside this contour. We denote it as the class of separating estimators. The second one is based on the norm of an arbitrary order. We denote it as the class of angular estimators. We show the asymptotic properties and the finite sample performances of both classes. We also illustrate the separating estimators with an empirical application to 21 worldwide financial market indexes.     

Predicting bank insolvencies using machine learning techniques

Proactively monitoring and assessing the economic health of financial institutions has always been the cornerstone of supervisory authorities. In this work, we employ a series of modeling techniques to predict bank insolvencies on a sample of US-based financial institutions. Our empirical results indicate that the method of Random Forests (RF) has a superior out-of-sample and out-of-time predictive performance, with Neural Networks also performing almost equally well as RF in out-of-time samples. These conclusions are drawn not only by comparison with broadly used bank failure models, such as Logistic, but also by comparison with other advanced machine learning techniques. Furthermore, our results illustrate that in the CAMELS evaluation framework, metrics related to earnings and capital constitute the factors with higher marginal contribution to the prediction of bank failures. Finally, we assess the generalization of our model by providing a case study to a sample of major European banks.     

The evolution of world trade from 1995 to 2014: A network approach

This paper employs network analysis to study world trade from 1995 to 2014. We focus on the main connective features of the world trade network (WTN) and their dynamics. Results suggest that countries’ efforts to attain the benefits of trade have resulted in an intertwined network that is increasingly dense, reciprocal, and clustered. However, these features do not correspond to a linear aggregation of the characteristics of its constituents (trade sectors). Trade linkages are distributed homogeneously among countries, but their intensity (i.e. their value) is highly concentrated in a small set of countries. The main connective features of the WTN were not affected by the 2007–2008 international financial crisis. However, we find that the crisis marks a turning point in the evolution of the WTN from a two-group (led by the US and Germany) to a three-group (led by the US, Germany, and China) hierarchical structure.     

The effects of handling outliers on the performance of bankruptcy prediction models

Ratio type financial indicators are the most popular explanatory variables in bankruptcy prediction models. These measures often exhibit heavily skewed distribution because of the presence of outliers. In the absence of clear definition of outliers, ad hoc approaches can be found in the literature for identifying and handling extreme values. However, it is not clear how these different approaches can affect the predictive power of models. There seems to be consensus in the literature on the necessity of handling outliers, at the same time, it is not clear how to define extreme values to be handled in order to maximize the predictive power of models. There are two possible ways to reduce the bias originating from outliers: omission and winsorization. Since the first approach has been examined previously in the literature, we turn our attention to the latter. We applied the most popular classification methodologies in this field: discriminant analysis, logistic regression, decision trees (CHAID and CART) and neural networks (multilayer perceptron). We assessed the predictive power of models in the framework of tenfold stratified crossvalidation and area under the ROC curve. We analyzed the effect of winsorization at 1, 3 and 5% and at 2 and 3 standard deviations, furthermore we discretized the range of each variable by the CHAID method and used the ordinal measures so obtained instead of the original financial ratios. We found that this latter data preprocessing approach is the most effective in the case of our dataset. In order to check the robustness of our results, we carried out the same empirical research on the publicly available Polish bankruptcy dataset from the UCI Machine Learning Repository. We obtained very similar results on both datasets, which indicates that the CHAID-based categorization of financial ratios is an effective way of handling outliers with respect to the predictive performance of bankruptcy prediction models.     

基于台风危害的厦门地区园林树木受损 原因研究

基于2016年9月16日1614号台风对厦门绿地的严 重影响，通过为期2个月对台风现场实地调研以及相关部门抢 险的数据统计，第一时间了解此次台风中厦门重要园林树种的 受害状况，分析树木在台风中的受损原因。台风灾害发生后给 厦门市的园林树木以及景观带来巨大的损害，因此对于厦门地 区园林树木受损原因的研究非常有必要。研究了受损原因后， 才可以在灾前以及灾后对重要的园林树木种进行栽培养护管 理，增加厦门绿化的抗灾能力以及灾后的及时恢复工作。     

On the heterogeneous short‐term effects of minimum wages on labor demand

A strand of the literature documents no effects or even positive effects of a higher minimum wage on employment. This evidence is frequently linked to the existence of monopsonistic labor markets or search frictions. However, empirical studies show that these findings could be related to a low short‐term minimum wage–employment elasticity in a competitive labor market. We show that mixed theoretical employment effects of a minimum wage policy can be predicted in the short term in assignment economies with price‐taker agents and no search frictions.     

Uncovering the global network of economic policy uncertainty

This study uncovers the static and dynamic network of economic policy uncertainty (EPU) across 17 developed and emerging economies. We build a centrality network using the minimal spanning tree (MST) as well as a dependency network using partial correlations. Results from the MST show that EPU exhibits some degree of geographical connections with EPUs in seven countries in the sample directly linked to the US EPU. Evidence from dynamic time-varying MST reveals that the nature and dominance of the EPU network have changed significantly over time. Further, the US and German EPUs dominate a close-knit global policy uncertainty network with the highest net (To and From) transmitter of information flow in the dependency network. Greece, Russia, and Brazil are the top three net receivers of information in the global network of EPU. The policy implication of these results relates to the renewed and ongoing international debate on policy coordination.