英文标题：
《Robust Productivity Analysis: An application to German FADN data》
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作者：
Mathias Kloss and Thomas Kirschstein and Steffen Liebscher and Martin
Petrick
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最新提交年份：
2019
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英文摘要：
Sources of bias in empirical studies can be separated in those coming from the modelling domain (e.g. multicollinearity) and those coming from outliers. We propose a two-step approach to counter both issues. First, by decontaminating data with a multivariate outlier detection procedure and second, by consistently estimating parameters of the production function. We apply this approach to a panel of German field crop data. Results show that the decontamination procedure detects multivariate outliers. In general, multivariate outlier control delivers more reasonable results with a higher precision in the estimation of some parameters and seems to mitigate the effects of multicollinearity.
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中文摘要：
实证研究中的偏差来源可以分为来自建模领域的偏差（例如多重共线性）和来自异常值的偏差。我们提出了两步解决这两个问题的方法。首先，通过多元离群点检测程序消除数据污染，其次，通过一致地估计生产函数的参数。我们将这种方法应用于一组德国田间作物数据。结果表明，去污程序可检测多变量异常值。一般来说，多元离群值控制在某些参数的估计中提供了更合理的结果和更高的精度，似乎可以减轻多重共线性的影响。
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分类信息：

一级分类：Economics 经济学
二级分类：General Economics 一般经济学
分类描述：General methodological, applied, and empirical contributions to economics.
对经济学的一般方法、应用和经验贡献。
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一级分类：Quantitative Finance 数量金融学
二级分类：Economics 经济学
分类描述：q-fin.EC is an alias for econ.GN. Economics, including micro and macro economics, international economics, theory of the firm, labor economics, and other economic topics outside finance
q-fin.ec是econ.gn的别名。经济学，包括微观和宏观经济学、国际经济学、企业理论、劳动经济学和其他金融以外的经济专题
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