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Pass‐Through and the Prediction of Merger Price Effects
Author(s) -
Miller Nathan H.,
Remer Marc,
Ryan Conor,
Sheu Gloria
Publication year - 2016
Publication title -
the journal of industrial economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.93
H-Index - 77
eISSN - 1467-6451
pISSN - 0022-1821
DOI - 10.1111/joie.12131
Subject(s) - monte carlo method , computer science , first pass , order (exchange) , econometrics , standard error , algorithm , mathematics , mathematical optimization , statistics , economics , arithmetic , finance
We use Monte Carlo experiments to study how pass‐through can improve merger price predictions, focusing on the first order approximation (FOA) proposed in Jaffe and Weyl [[Jaffe, S., 2013]]. FOA addresses the functional form misspecification that can exist in standard merger simulations. We find that the predictions of FOA are tightly distributed around the true price effects if pass‐through is precise, but that measurement error in pass‐through diminishes accuracy. As a comparison to FOA, we also study a methodology that uses pass‐through to select among functional forms for use in simulation. This alternative also increases accuracy relative to standard merger simulation and proves more robust to measurement error.