EconPapers    
Economics at your fingertips  
 

Robust Productivity Analysis: An application to German FADN data

Mathias Kloss (), Thomas Kirschstein, Steffen Liebscher and Martin Petrick ()

Papers from arXiv.org

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.

Date: 2019-02, Revised 2019-02
New Economics Papers: this item is included in nep-ecm and nep-eff
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
http://arxiv.org/pdf/1902.00678 Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1902.00678

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().

 
Page updated 2021-01-25
Handle: RePEc:arx:papers:1902.00678