基于生命周期模型的农户储蓄变动机制研究

运用1997-2006年省际动态面板数据研究了我国农村居民储蓄率变动的决定因素,并检验了永久收入假说(生命周期理论)对中国农村居民的适应性。实证研究发现,我国农村家庭储蓄率持续攀升以及时空差异的主要原因在于农村居民储蓄习惯(滞后储蓄率)、收入增长率、实际利率和宏观经济波动;但与人口年龄结构相关的因素对农村居民储蓄率变动影响较小,实证结果部分地支持生命周期理论假说。因此,从中短期来看,我国农村居民家庭储蓄率依然较高,应设计出刺激农民消费、减少储蓄的中短期政策组合,同时从制度层面来理顺农村储蓄资金转化为投资的渠道。     

收入分配对农村居民恩格尔系数的影响研究

恩格尔系数是衡量居民生活水平的重要指标.本文通过理论分析和实证研究发现:收入差距扩大降低了我国农村居民的恩格尔系数,提高了农民总体生活水平,收入差距对总体生活水平上升的贡献度达到1/3,从而出现"分配越不平等,农民生活水平越高"的悖论.因此在对农民生活做(福利)评价时,必须同时考虑总体指标和分配指标,在发展农村经济、提高农民生活水平过程中,必须重视收入分配,兼顾公平与效率,才能实现农民的福利最大化.     

基于分析型KANO模型的物流服务质量提升决策方法

针对Kano模型定性、分类准则主观的局限,提出一种分析型Kano模型的设计方法,通过构建Kano满意度指标和重要度指标,实现质量因素的客观化分类;设计Kano模型决策指标,获取提升服务质量属性的优先权。在分析基于分析型Kano模型的物流服务质量提升决策过程的基础上,从实证视角研究了模型的具体应用问题。     

Lost in a black-box? Interpretable machine learning for assessing Italian SMEs default

Academic research and the financial industry have recently shown great interest in Machine Learning algorithms capable of solving complex learning tasks, although in the field of firms' default prediction the lack of interpretability has prevented an extensive adoption of the black-box type of models. In order to overcome this drawback and maintain the high performances of black-boxes, this paper has chosen a model-agnostic approach. Accumulated Local Effects and Shapley values are used to shape the predictors' impact on the likelihood of default and rank them according to their contribution to the model outcome. Prediction is achieved by two Machine Learning algorithms (eXtreme Gradient Boosting and FeedForward Neural Networks) compared with three standard discriminant models. Results show that our analysis of the Italian Small and Medium Enterprises manufacturing industry benefits from the overall highest classification power by the eXtreme Gradient Boosting algorithm still maintaining a rich interpretation framework to support decisions.