Health and political regimes: Evidence from quantile regression

This study investigates how political regimes affect health conditions such as infant and child mortality rates and life expectancy using data from 180 countries observed between 1960 and 2013. Panel quantile regression is used to examine the effects at different intervals throughout the distribution of health outcomes. The estimation results indicate that democracy has significant positive effects on health outcomes and that its impacts are greater when health outcomes are worse. These results are robust to different democracy and health indices. The effects of different types of democracies and dictatorships are also considered, that is parliamentary, mixed (semi-presidential) and presidential democracies, and civilian, military and royal dictatorships. The parliamentary form of democracy has the largest positive impact on health outcomes at the worst quantile of health outcomes, although the difference in the impacts of the three types of democracies is not necessarily large. Furthermore, all types of dictatorships have a negative impact on health outcomes, with military dictatorship having the worst outcome when health outcomes are worse. Finally, the effects of democratization on health outcomes are significantly positive when the health outcomes are worse.     

Measuring Inflation Anchoring and Uncertainty: A U.S. and Euro Area Comparison

We use several U.S. and euro‐area surveys of professional forecasters to estimate a dynamic factor model of inflation featuring time‐varying uncertainty. We obtain survey‐consistent distributions of future inflation at any horizon, both in the U.S. and the euro area. Equipped with this model, we propose a novel measure of the anchoring of inflation expectations that accounts for inflation uncertainty. Our results suggest that following the Great Recession, inflation anchoring improved in the United States, while mild de‐anchoring occurred in the euro area. As of our sample end, both areas appear to be almost equally anchored.     

Are USDA reports still news to changing crop markets?

This study investigates whether major USDA reports still provide important news to changing crop markets. The news component of each report, or market “surprise,” is measured as a difference between the USDA estimate and its private expectation in corn, soybeans, and wheat markets. Changes in the relevance of USDA information are assessed by examining changes in the magnitude of market surprises and shifts in the futures price reaction to these surprises, which isolates the impact of each report. The stable size of market surprises over time suggests that competition from alternative data sources has not reduced the news component of USDA crop reports. Increasing price reaction to most reports, including those facing competition from alternative information sources, suggests that value of public information may be enhanced in uncertain markets affected by structural changes.     

互联网金融规制与市场有效性改善*——来自中国网络借贷行业的证据

中国的互联网金融经历了一个先创新发展后监管规制的过程，本文通过对网络借贷行业的实证研究，分析监管规则的出台实施对新型互联网融资市场有效性的影响。实证结果表明，网络借贷平台资金的银行存管、持有ICP证等强制性规定缓解了投资者、平台企业与借款人之间的信息不对称，对克服早期平台间通过竞相承诺高回报率来吸引投资者导致的“柠檬市场”问题、对解决网贷市场上缺乏平台风险性高低有效信号的问题都起到了正向作用，有助于互联网融资市场的有效性提高。     

Best classification algorithms in peer-to-peer lending

A proper credit scoring technique is vital to the long-term success of all kinds of financial institutions, including peer-to-peer (P2P) lending platforms. The main contribution of our paper is the robust ranking of 10 different classification techniques based on a real-world P2P lending data set. Our data set comes from the Lending Club covering the 2009–2013 period, which contains 212,252 records and 23 different variables. Unlike other researchers, we use a data sample which contains the final loan resolution for all loans. We built our research using a 5-fold cross-validation method and 6 different classification performance measurements. Our results show that logistic regression, artificial neural networks, and linear discriminant analysis are the three best algorithms based on the Lending Club data. Conversely, we identify k-nearest neighbors and classification and regression tree as the two worst classification methods.     

What is happening in Paris? Airbnb,hotels, and the Parisian market: A case study

Airbnb has been the subject of heated discussions among tourism and hospitality scholars. In order to understand the true impact of P2Psharing on the tourism and hospitality landscape, it is important to understand that market in detail first. Therefore, in this paper, we explore the development of the Airbnb market over the last seven years in Paris, the most popular destination for Airbnb guests with more than 40,000 accommodation rentals. This research note is a summary of our findings on the Paris market. Our study shows different growth and seasonality patterns for Airbnb and hotels and dissimilarities in the geographical location of the offers. The findings indicate that the two products are not in direct competition and that their relationship might be more complex than previously thought. We trust that exploring the nature of the competitive relationship would be a valuable avenue for further investigation.     

The mechanism of credit risk contagion among internet P2P lending platforms based on a SEIR model with time-lag

The credit risk contagion of Internet peer-to-peer (P2P) lending platforms is an important part of Internet financial risk management and supervision. This study analyzes the contagion path of credit risk in Internet P2P lending. Based on complex network theory and the theory of infectious disease dynamics, the characteristics of Internet P2P lending development are combined to construct a SEIR model of credit risk transmission among Internet P2P lending platforms with time lag, and the robustness of the model is analyzed and proven. The influence of platform correlations, the susceptible immune rate, the platform elimination rate, contagion latency, the saturation coefficient, and the susceptibility input rate on credit risk contagion behavior among Internet P2P lending platforms is analyzed, using the equilibrium point and threshold value. The impact of each variable is analyzed by simulation. Corresponding countermeasures and suggestions are proposed to prevent and control credit risk contagion among these platforms.     

Increasing linkages among European regions. The role of sectoral composition

This paper analyses changes in economic regional interlinkages in Europe over time and investigates the factors that could explain the dynamics of these changes. Our four main findings are the following: (i) we detect a significant surge in regional synchronisation after the Great Recession; (ii) we identify the regions most interrelated with the rest of Europe, namely, Ile de France, Inner London and Lombardia; (iii) we find that sectoral composition explains regional synchronisation in Europe, mainly after the Great Recession and (iv) we document that sectoral composition has important implications for aggregate economic fluctuations, in particular, that similarities in services-related sectors across regions explain a nonlinear relationship between sectoral composition and regional business cycle synchronisation. We also propose a new method to measure time-varying synchronisation in small samples that combines regime-switching models and dynamic model averaging.     

Change-point detection for expected shortfall in time series

Expected shortfall (ES) is a popular risk measure and plays an important role in risk and portfolio management. Recently, change-point detection of risk measures has been attracting much attention in finance. Based on the self-normalized CUSUM statistic in Fan, Glynn and Pelger (2018) and the Wild Binary Segmentation (WBS) algorithm in Fryzlewicz (2014), this paper proposes a variant WBS procedure to detect and estimate change points of ES in time series. The strengthened Schwarz information criterion is also introduced to determine the number of change points. Monte Carlo simulation studies are conducted to assess the finite-sample performance of our variant WBS procedure about ES in time series. An empirical application is given to illustrate the usefulness of our procedure.     

高校女生体育课堂教学的现状及对策

调查发现女生中只有33．97％对体育课有兴趣，原因是：体育意识不成熟、教材存在重复现象、有严重的心理障碍、没有突破传统的教学方法、课外活动缺乏行之有效的组织管理等。应采取的措施有：明确高校体育教育理念、加强思想教育、改革体育教学模式、合理选择教学内容和方法等。