# Robust Productivity Analysis: An application to German FADN data

## TL;DR

This paper introduces a two-step method combining outlier detection and robust parameter estimation to improve productivity analysis accuracy using German FADN data, addressing biases from outliers and multicollinearity.

## Contribution

It presents a novel approach that integrates multivariate outlier detection with consistent production function estimation for more reliable empirical results.

## Key findings

- Outlier detection effectively identifies multivariate outliers.
- Decontamination improves parameter estimation precision.
- Method reduces bias from multicollinearity in productivity analysis.

## 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.