资本下乡：进入模式选择及其影响因素

进入模式是经济组织配置资源的重要手段。以工商资本对农户经营活动的控制权程度为依据,将工商资本投资农业的进入模式划分为“松散型”、“半紧密型”、“紧密型”三种类型。利用对广东、湖北、江苏以及云南四省151个投资主体的调查数据,采用有序逻辑回归模型分析生产因素、市场因素、制度因素以及生产技术传递能力对工商资本投资农业的进入模式选择的影响。结果表明：耕地资源优势越弱,工商资本投资农业选择紧密型进入模式的可能性越高;市场潜力越大,越倾向于选择紧密型进入模式;非正式制度越不利,选择紧密型进入模式的可能性越低;生产技术的传递能力越弱,越倾向于选择紧密型进入模式。     

农村老年人日常生活家庭照料与社会照料关系研究——基于多层回归模型的分析

文章利用中国健康与养老追踪调查 2011—2012 年的基线调查数据,运用多层回归模型,通过分析社会照料对农村老年人家庭照料获得的影响,探讨家庭照料与社会照料的关系。 回归估计结果显示,个人层次上的年龄、居住状况、失能情况和社区层次上的老龄补贴、经济状况对农村老年人获得家庭照料有不同程度的影响,而代表社会照料的变量在统计上均不显著。 回归估计结果说明,社会照料并不能对缺位的家庭照料起到补充作用,更不会起到替代作用,两者呈现一种非此即彼的“无关系”。 文章的政策启示是,打破家庭照料与社会照料的壁垒,构建一个“流动”的老年照料体系。     

Improving logistic regression on the imbalanced data by a novel penalized log-likelihood function

Logistic regression is estimated by maximizing the log-likelihood objective function formulated under the assumption of maximizing the overall accuracy. That does not apply to the imbalanced data. The resulting models tend to be biased towards the majority class (i.e. non-event), which can bring great loss in practice. One strategy for mitigating such bias is to penalize the misclassification costs of observations differently in the log-likelihood function. Existing solutions require either hard hyperparameter estimating or high computational complexity. We propose a novel penalized log-likelihood function by including penalty weights as decision variables for observations in the minority class (i.e. event) and learning them from data along with model coefficients. In the experiments, the proposed logistic regression model is compared with the existing ones on the statistics of area under receiver operating characteristics (ROC) curve from 10 public datasets and 16 simulated datasets, as well as the training time. A detailed analysis is conducted on an imbalanced credit dataset to examine the estimated probability distributions, additional performance measurements (i.e. type I error and type II error) and model coefficients. The results demonstrate that both the discrimination ability and computation efficiency of logistic regression models are improved using the proposed log-likelihood function as the learning objective.     

Regression models using the LINEX loss to predict lower bounds for the number of points for approximating planar contour shapes

Researchers in statistical shape analysis often analyze outlines of objects. Even though these contours are infinite-dimensional in theory, they must be discretized in practice. When discretizing, it is important to reduce the number of sampling points considerably to reduce computational costs, but to not use too few points so as to result in too much approximation error. Unfortunately, determining the minimum number of points needed to achieve sufficiently approximate the contours is computationally expensive. In this paper, we fit regression models to predict these lower bounds using characteristics of the contours that are computationally cheap as predictor variables. However, least squares regression is inadequate for this task because it treats overestimation and underestimation equally, but underestimation of lower bounds is far more serious. Instead, to fit the models, we use the LINEX loss function, which allows us to penalize underestimation at an exponential rate while penalizing overestimation only linearly. We present a novel approach to select the shape parameter of the loss function and tools for analyzing how well the model fits the data. Through validation methods, we show that the LINEX models work well for reducing the underestimation for the lower bounds.     

The modified slash Lindley–Weibull distribution with applications to nutrition data

This work presents an extension of the slash Lindley–Weibull distribution, of which it can be considered a modification. The new family is obtained by using the quotient of two independent random variables: a two-parameter Lindley–Weibull distribution divided by a power of the exponential distribution with parameter equal to 2. We present the pdf and cdf of the new distribution, analyzing their risk functions. Some statistical properties are studied and the moments and coefficients of asymmetry and kurtosis are shown. The parameter estimation problem is carried out by the maximum likelihood method. The method is assessed by a Monte Carlo simulation study. We use nutrition data, which are characterized by high kurtosis, to illustrate the usefulness of the proposed model.     

饭店类上市公司财务危机预警研究

财务危机预警分析正在成为企业提高管理水平的重要工具。我国上市饭店企业数量减少,经济效益总体差强人意。企业出现危机能够通过财务信息反映出来,将逻辑回归预警模型引入饭店企业管理中,有助于企业建立相应的预警应对机制,及时采取措施,以减少或消除财务危机带给企业的危害,提高上市饭店企业的竞争力。     

Misspecification of a binary dependent variable in the logistic model controlling for the repeated longitudinal measures

Many medical applications are interested to know the disease status. The disease status can be related to multiple serial measurements. Nevertheless, owing to various reasons, the binary outcome can be measured incorrectly. The estimators derived from the misspecified outcome can be biased. This paper derives the complete data likelihood function to incorporate both the multiple serial measurements and the misspecified outcome. Owing to the latent variables, EM algorithm is used to derive the maximum-likelihood estimators. Monte Carlo simulations are conducted to compare the impact of misspecification on the estimates. A retrospective data for the recurrence of atrial fibrillation is used to illustrate the usage of the proposed model.     

国际碳市场价格驱动力研究——以欧盟排放交易体系为例

探索碳市场价格驱动力是能源经济领域关注的焦点问题之一。基于欧盟排放交易体系（EU ETS）碳期货价格,综合运用结构性断点检验、协整技术和岭回归方法分析碳市场价格驱动因素和驱动模式。结果表明：2007年巴厘行动计划、2008年全球金融危机和2011年欧债危机,均对碳市场价格走势产生了显著影响,导致了碳市场价格发生结构性变化;碳市场价格与能源价格、工业生产、气温条件等因素间存在协整关系;自2009年10月以来,碳市场价格观测值低于均衡值,未来碳市场价格仍将倾向于贬值。     

数字素养与农户收入差距:加剧还是缓解?——基于中西部地区的微观证据

数字经济时代,数字贫困问题对农户收入差距的影响引发新的关注。基于2021年中国农村经济与农村金融数据,采用再中心化响应函数(RIF)回归方法探究数字素养对农户收入差距的影响。研究发现:(1)数字素养显著扩大了农户收入差距,并具备明显的马太效应,高数字素养能够攫取更多数字红利,而低数字素养更容易陷入“数字贫困”状态,无法发挥出数字经济的增收效应。(2)应用数字素养会显著加剧农户收入差距,而通用数字素养对农户收入差距的影响并不显著。这种情况也得以印证,现阶段数字“接入鸿沟”已经得以解决,数字“应用鸿沟”差距逐渐凸显。(3)数字素养能够缩小农户财产性收入差距,但会显著扩大工资性收入差距与非农经营性收入差距。脱贫地区受到数字素养的马太效应更强,农户收入差距明显加剧。研究结论证实了我国农村内部存在明显的数字鸿沟,同时数字贫困问题加剧了农户收入差距。新发展阶段我国数字乡村战略的推进不仅要注重农村低收入群体以及弱势群体数字素养的培育,更要加快构建农村数字经济的包容性发展路径,助力农民农村共同富裕的实现。     

数字金融对农户气候适应性行为的影响

气候适应性技术采用率低下已成为制约农业可持续发展的重要因素,数字金融可能影响农户的气候适应性行为。基于河南、陕西、山西三省1 384份农户微观调查数据,运用内生转换回归模型构建反事实分析框架,实证分析数字金融使用对农户气候适应性行为的影响效应及其作用机制。研究发现:使用数字金融能显著促进农户气候适应性技术采纳行为,具体表现为在反事实假设下,使用数字金融的农户若未使用其气候适应性技术采纳程度将下降;未使用数字金融的农户如果使用了,其气候适应性技术采纳程度将上升。机制分析表明,数字金融能够提高借贷易得性与信息易得性,进而促进农户采纳气候适应性行为,农户对于金融包容性的认知能够正向增强数字金融对农户气候适应性行为的影响效应。异质性分析表明,数字金融使用对于资本型适应性行为影响的边际效应最大,在反事实假设下也表现为数字金融使用对农户资本型适应性行为提升效果最强;数字金融对农业收入占家庭收入比率较高、接受过培训的农户采纳气候适应性技术促进效应更高。