EU Merger Policy Predictability Using Random Forests

49 Pages Posted: 30 Apr 2019

See all articles by Pauline Affeldt

Pauline Affeldt

German Institute for Economic Research (DIW Berlin); Technische Universität Berlin (TU Berlin)

Multiple version iconThere are 2 versions of this paper

Date Written: April 2019

Abstract

I study the predictability of the EC’s merger decision procedure before and after the 2004 merger policy reform based on a dataset covering all affected markets of mergers with an official decision documented by DG Comp between 1990 and 2014. Using the highly flexible, non-parametric random forest algorithm to predict DG Comp’s assessment of competitive concerns in markets affected by a merger, I find that the predictive performance of the random forests is much better than the performance of simple linear models. In particular, the random forests do much better in predicting the rare event of competitive concerns. Secondly, postreform, DG Comp seems to base its assessment on a more complex interaction of merger and market characteristics than pre-reform. The highly flexible random forest algorithm is able to detect these potentially complex interactions and, therefore, still allows for high prediction precision.

Keywords: Merger policy reform, DG Competition, Prediction, Random Forests

JEL Classification: K21, L40

Suggested Citation

Affeldt, Pauline, EU Merger Policy Predictability Using Random Forests (April 2019). DIW Berlin Discussion Paper No. 1800, 2019. Available at SSRN: https://ssrn.com/abstract=3379536 or http://dx.doi.org/10.2139/ssrn.3379536

Pauline Affeldt (Contact Author)

German Institute for Economic Research (DIW Berlin) ( email )

Mohrenstraße 58
Berlin, 10117
Germany

Technische Universität Berlin (TU Berlin) ( email )

Straße des 17
Juni 135
Berlin, 10623
Germany

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