摘要
政府统计数据质量是当前各界关注的热点问题,如何采用严谨的诊断方法,对我国统计数据进行科学的评估具有重要的现实意义。稳健回归方法可使求出的回归估计不受异常值的强烈影响,并且能更好地识别异常点。本文首次运用基于稳健MM估计的异常值诊断方法,在生产函数模型的框架下,分别使用两种不同的劳动投入数据,对改革开放以来我国GDP数据质量进行了评估。结果表明,基于稳健MM估计的异常值诊断方法可有效地解决传统方法容易出现的多个异常点的掩盖现象,改革以来我国的GDP数据是相对可靠的。
Statistical data quality is a widespread concern issue. It is significant to evaluate China＇s statistics scientifically using rigorous diagnostic methods. Robust regression is resistant to the influence of outliers and can be used as a good tool identifying outliers. Under the framework of production function model and using two different labor input data, this paper first applies robust MM estimator as a detection tool of outliers to assess China＇s GDP data quality since reform. The results show that the problem of masking effect which exists in traditional methods can be effectively solved and China＇s GDP data since reform is relatively reliable.
出处
《统计研究》
CSSCI
北大核心
2010年第12期16-22,共7页
Statistical Research
关键词
统计数据质量
稳健MM估计
异常值诊断
Statistical data quality： Robust MM estimator
Outliers detection
