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Robust Productivity Analysis An application to German FADN data

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Document pages: 27 pages

Abstract: Sources of bias in empirical studies can be separated in those coming fromthe modelling domain (e.g. multicollinearity) and those coming from outliers.We propose a two-step approach to counter both issues. First, bydecontaminating data with a multivariate outlier detection procedure andsecond, by consistently estimating parameters of the production function. Weapply this approach to a panel of German field crop data. Results show that thedecontamination procedure detects multivariate outliers. In general,multivariate outlier control delivers more reasonable results with a higherprecision in the estimation of some parameters and seems to mitigate theeffects of multicollinearity.

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