User Fee Abolition and the Demand for Public Health Care

This research examines the effect of the abolition of user fees in South Africa, a policy implemented in 1994 for uninsured children under the age of six and the elderly uninsured, as well as pregnant and nursing mothers. The analysis focuses on the implementation of the policy and the use of curative public healthcare services by children following strict and fuzzy regression discontinuity designs. The estimates point to statistically insignificant average and local average policy effects, even though the policy appears to have been implemented reasonably effectively, albeit imperfectly. In other words, the policy did not, on average, affect the use of curative public healthcare, at least for those children who should have benefited from the policy.     

A new multiscale decomposition ensemble approach for forecasting exchange rates

Due to the high complexity and strong nonlinearity nature of foreign exchange rates, how to forecast foreign exchange rate accurately is regarded as a challenging research topic. Therefore, developing highly accurate forecasting method is of great significance to investors and policy makers. A new multiscale decomposition ensemble approach to forecast foreign exchange rates is proposed in this paper. In the approach, the variational mode decomposition (VMD) method is utilized to divide foreign exchange rates into a finite number of subcomponents; the support vector neural network (SVNN) technique is used to model and forecast each subcomponent respectively; another SVNN technique is utilized to integrate the forecasting results of each subcomponent to generate the final forecast results. To verify the superiority of the proposed approach, four major exchange rates were chosen for model comparison and evaluation. The experimental results indicate that our proposed VMD-SVNN-SVNN multiscale decomposition ensemble approach outperforms some other benchmarks in terms of forecasting accuracy and statistical tests. This demonstrates that our proposed VMD-SVNN-SVNN multiscale decomposition ensemble approach is promising for forecasting foreign exchange rates.     

不同利率对我国上市银行信用风险的影响

对我国16家上市银行进行的实证检验表明：3个月同业拆借利率与银行信用风险显著正相关，人民币5年期基准存款利率与银行信用风险的关系不确定，人民币1年期基准存款利率与银行信用风险显著正相关，贷款利息收入占贷款比例与银行信用风险显著负相关。     

Forecasting China's GDP growth using dynamic factors and mixed-frequency data

Forecasting GDP growth is important and necessary for Chinese government to set GDP growth target. To fully and efficiently utilize macroeconomic and financial information, this paper attempts to forecast China's GDP growth using dynamic predictors and mixed-frequency data. The dynamic factor model is first applied to select dynamic predictors among large amount of monthly macroeconomic and daily financial data and then the mixed data sampling regression is applied to forecast quarterly GDP growth based on the selected monthly and daily predictors. Empirical results show that forecasts using dynamic predictors and mixed-frequency data have better accuracy comparing to traditional forecasting methods. Moreover, forecasts with leads and forecast combination can further improve forecast performance.     

Value for money in energy efficiency retrofits in Ireland: grant provider and grant recipients

The Sustainable Energy Authority of Ireland (SEAI) administers the Better Energy Homes scheme to provide a financial incentive for home owners to engage in energy efficiency retrofits. This study analyses data from the scheme and Building Energy Rating data for participants to the scheme to examine the value for money achieved by households. In addition, this research identifies which retrofit combinations provide greatest value for money, in terms of energy efficiency gains, for the grant provider. We utilize an error-in-variables approach to model the variation in benefits accruing to households of varying characteristics. We find that household and grant provider surplus can be maximized in the short term by retrofitting less energy efficient and larger homes, timber or steel frame homes and houses rather and apartments. The types of retrofits leading to the greatest surplus for both household and grant provider include cavity wall insulation paired with either a boiler with heating controls or heating controls only retrofit.     

Assessing impact of regulatory risk on shareholders’ wealth

Research indicates that regulatory risk increases required return on investment by investors and causes underinvestment in industries with high sunk costs. The effects of regulatory changes may be measured by estimating the abnormal returns associated with the event. The results may suggest to regulators what should be encouraged or avoided. This article utilizes a fixed effects regression to examine abnormal returns from changes in Philippine nationalization regulations. The results are consistent with extant literature. Supreme Court decisions, which increased uncertainty and regulatory risk, produced negative abnormal returns. The initial release of draft implementing rules did not produce statistically significant effects, but a succeeding draft favouring liberalization, produced positive abnormal returns.     

新型农村社会养老保险对贫困群体生活的影响——基于 CHARLS2011～2015面板数据的分析

本文探讨了新型农村社会养老保险是否对低收入的贫困群体的生活产生更显著的效用,从而论证该制度是否助力扶贫工作。因为养老金是收入的组成部分,而收入有边际效用递减的特点,因此养老金对不同收入群体的效用可能存在差别。本文按家庭人均收入水平区分贫困与非贫困群体,基于CHARLS 2011 -2015年数据,采用多维贫困指标,使用断点回归发现新农保养老金对贫困参保群体在食品消费方面产生更显著的效用,有助于扶贫工作。并使用含复杂时变效应的二元变量模型,进一步发现该效用在养老金开始发放时显现。本文结论有利于认识新农保政策对扶贫的作用,并将扶贫工作和已有的社会保险政策结合,以提高扶贫工作效率。     

提升算法对传统车险索赔频率建模模型的改进——基于我国五省交强险保单数据

广义线性模型作为非寿险定价的经典模型,在非寿险定价中得到了广泛的应用。近年来,以提升算法为代表的机器学习算法在保险领域取得了很好的效果,为保险产品定价提供了一种新的选择。本文将提升算法思想分别融入到回归树模型和广义线性模型(GLM)中去,用得到的新模型对我国车险索赔频率进行预测建模分析,并与传统的回归树模型和GLM进行比较。结果表明,加入提升算法后传统车险索赔频率建模模型的效果得到了很大的改善,并且在不存在过拟合的前提下,随着模型深度和迭代次数的增加,模型的效果也在不断优化。     

投资者关注对金银期货市场收益影响的时频分位研究

根据Google投资者关注度指数和金银期货市场交易数据,构建基于小波分解序列的时频门限自回归分布滞后模型,通过分位数模型参数估计,基于时域与频域联合分析视角,考量投资者关注度对金银期货市场收益的影响。结果表明：投资者关注度对金银期货市场的影响具有异质性;在低频域内,投资者关注度对金银期货市场影响相对较小;极端分位数水平下,投资者关注度对金银期货市场收益影响的时效性较短,投资者关注度对白银期货市场收益的影响较弱。