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arXiv · 1902.00678

Robust Productivity Analysis: An application to German FADN data

Mathias Kloss·Thomas Kirschstein·Steffen Liebscher·Martin Petrick
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Abstract

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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Mathias Kloss, Thomas Kirschstein, Steffen Liebscher, Martin Petrick. 2019-02-13. Robust Productivity Analysis: An application to German FADN data. https://arxiv.org/abs/1902.00678

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