  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.
