转移支付收入对县(市)经济增长的实证研究——基于吉林省2005年50个县(市)数据的分析

以吉林省2005年50个县(市)为样本,选用转移支付收入与经济增长(GDP)为变量,运用线性回归方法可得出如下结论:转移支付收入增加会促进地区的经济增长;对中心城市的促进作用比非中心城市的促进作用显著。应加大省级政府对县(市)政府的财政转移支付力度,促进中心城市加强对所属县(市)的横向财政转移支付水平。     

How do “gatekeepers” affect credit risk?

This study investigates the relationship between auditor tenure and credit default swap (CDS) spreads of U.S. firms based on quantile regression. After allowing for common determinants of CDS spreads, auditor tenure exerts both statistically and economically significant additional impacts on the CDS market. Furthermore, there are differential effects of common CDS spread determinants and auditor tenure. While common determinants of CDS spreads (e.g., leverage, volatility, risk free rate, credit ratings, and earnings) have monotonically increasing impacts when CDS spreads (and their changes) are increasingly higher, auditor tenure primarily has the impact when CDS spreads are of low or median levels for less risky firms.     

Labor market analysis through transformations and robust multivariate models

The work presents a robust approach to labor share analysis. The estimate of labor share presents various complexities related to the nature of the data sets to be analyzed. Typically, labor share is evaluated by using discriminant analysis and linear or generalized linear models, that do not take into account the presence of possible outliers. Moreover, the variables to be considered are often characterized by a high dimensional structure. The proposed approach has the objective of improving the estimation of the model using robust multivariate regression techniques and data transformation.     

Quantile regression for the qualifying match of GEFCom2017 probabilistic load forecasting

We present a simple quantile regression-based forecasting method that was applied in the probabilistic load forecasting framework of the Global Energy Forecasting Competition 2017 (GEFCom2017). The hourly load data are log transformed and split into a long-term trend component and a remainder term. The key forecasting element is the quantile regression approach for the remainder term, which takes into account both weekly and annual seasonalities, such as their interactions. Temperature information is used only for stabilizing the forecast of the long-term trend component. Information on public holidays is ignored. However, the forecasting method still placed second in the open data track and fourth in the definite data track, which is remarkable given the simplicity of the model. The method also outperforms the Vanilla benchmark consistently.     

Data preprocessing and quantile regression for probabilistic load forecasting in the GEFCom2017 final match

Team QUINKAN competed in the GEFCom2017 final match of hierarchical probabilistic load forecasting by adopting the quantile regression method using the R package quantreg. The weather stations were clustered into 11 groups, from which an optimal one was chosen for each load meter using the boosting method. The load meter records were cleaned and/or supplemented by various methods in order to secure robust quantile predictions. The variation in the regression formulas was kept as small as possible by introducing measures for suppressing prediction instability, although special formulas were employed for loading meters that were of an industrial nature. Several procedures were applied to help improve the accuracy, such as the smoothing of season transitions, coarse graining of the relative humidity, the use of load-oriented day-type definition, the averaging of weather data, and outlier removal.     

Assessing the influence of external factors on agricultural production in Brazil

This article uses 2017 Brazilian agricultural census data (aggregated at county level) to evaluate the impact of external factors on agricultural efficiency in Brazil. The external factors are defined as access to credit, participation in cooperatives, proportion of literacy, technical assistance, and environmental production practices. All variables are transformed as the log of the municipal (county) rank. The response variable is defined as the free disposal hull (FDH) conditional ratio and a one-inflated beta regression is estimated. The results show that in counties where the FDH ratio is equal to 1, cooperatives have the biggest impact on efficiency, and where the FDH ratio is not equal to 1, environmental practices, which are most often associated with technology, have the largest effect on efficiency. Finally, the regions that are expected to see the largest gains in efficiency with increases in external factors are the Northeastern and the Northern regions of Brazil.     

中等城市绿色交通系统分类评定模型建构

根据以往我国城市绿色交通的调查结论,设计调查问卷,调查之后进行分析。运用Logit回归模型,建立一个专门针对二元因变量的Binary Logit模型,得出以下结论:收入水平、交通拥堵状况、对小汽车的态度和低碳环保意识4个变量因为统计检验不显著而未能进入最终模型。同时,油价、停车费、居所附近的直达公交线路、居所附近的轨道交通、公共交通乘坐舒适度、工作日平均出行距离与居民绿色出行方式的选择具有显著相关关系。     

Aggregate volatility expectations and threshold CAPM

We propose a volatility-based capital asset pricing model (V-CAPM) in which asset betas change discretely with respect to changes in investors’ expectations regarding near-term aggregate volatility. Using a novel measure to proxy uncertainty about expected changes in aggregate volatility, i.e. monthly range of the VIX index (RVIX), we find that portfolio betas change significantly when uncertainty about aggregate volatility expectations is beyond a certain threshold level. Due to changes in their market betas, small and value stocks are perceived as riskier than their big and growth counterparts in bad times, when uncertainty about aggregate volatility expectations is high. The proposed model yields a positive and significant market risk premium during periods when investors do not expect significant uncertainty in near-term aggregate volatility. Our findings support a volatility-based time-varying risk explanation.     

温州经济发展与温州港口吞吐量关系研究

以温州港为例,适当选取一定的城市经济指标和港口经济指标,在分析温州港经济发展的基础上,借助相关分析与回归分析等定量分析方法,系统的分析了温州港对温州城市经济发展的影响,旨在促进温州的发展与建设。     

Predicting bankruptcy in the Texas nursing facility industry

Approximately 50% of nursing facilities in Texas petitioned for bankruptcy during the 1998–2004 period. Using a logit regression model tested for robustness, we find nursing facilities that are profit-seekers, chain members, pay higher than average wage rates, accept more intensive-care residents and obtain a larger than average portion of their funding from public sources are highly vulnerable to negative changes in regulatory policy decisions on Medicare and Medicaid reimbursement. Larger facilities having higher than average occupancy rates and quality of care are less susceptible to adverse decisions. The model correctly classifies a facility as either bankrupt or solvent in about 75% of cases. We also examine the duration of bankruptcy using accelerated failure-time models. It appears that the duration of bankruptcy depends on location, out-of-state ownership, length of ownership, volume of resident days supplied, total cost and proportion of revenues from Medicaid.