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Robust Productivity Analysis: An application to German FADN data

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  • Mathias Kloss
  • Thomas Kirschstein
  • Steffen Liebscher
  • Martin Petrick

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. "Robust Productivity Analysis: An application to German FADN data," Papers 1902.00678, arXiv.org, revised Feb 2019.
    • Handle: RePEc:arx:papers:1902.00678
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