• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>International Journal of Statistics and Applications >Household Poverty-Risk Analysis and Prediction Using Bayesian Ordinal Probit Models
【24h】

Household Poverty-Risk Analysis and Prediction Using Bayesian Ordinal Probit Models

机译：贝叶斯有序概率模型的家庭贫困风险分析与预测

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Though the rate of poverty in Ghana has consistently declined over the years, some parts of the country still record substantially high figures [1], and this is a major concern for stake holders. Previous research to identify causal factors has commonly used the binary logit or probit models. These models, however, mask the effect of important intermediate information during the binary transformation of the response variable. This has the potential to misestimate the probability of poverty. In this study, the ordered probit model was used, thus creating a framework that includes the ordinal nature of poverty severity. The model was based on the round 6 dataset of the Ghana Living Standards Survey. Our findings show that poor and extremely poor were negatively affected by rural location, illiteracy, and Savannah ecological zone. Policies to eradicate poverty must therefore aim at optimizing these significant variables contributions to welfare conditions in the country.
机译：尽管加纳的贫困率多年来一直在下降，但该国某些地区的贫困率仍然很高[1]，这是利益相关者所关注的主要问题。先前确定因果关系的研究通常使用二进制logit或probit模型。但是，这些模型掩盖了重要的中间信息在响应变量的二进制转换过程中的作用。这可能会错误估计贫困的可能性。在这项研究中，使用了有序的概率模型，从而创建了一个包含贫困严重程度的序数性质的框架。该模型基于加纳生活水平调查的第6轮数据集。我们的发现表明，贫困地区和极度贫困地区受到农村地区，文盲和萨凡纳生态区的不利影响。因此，消除贫困的政策必须旨在优化对国家福利条件的这些重要变量。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号