Estimating Market Power with Weak A Priori Information: An Exploratory Approach to the Model-Specification Problem

Estimating Market Power with Weak A Priori Information: An Exploratory Approach to the... Choosing the wrong theoretical model to describe an industry’s behavior may lead to biased estimates of the degree of market power. This paper presents a two-step, data-driven methodology to reduce the risk of mis-specification bias. In the first step, a sliced inverse regression identifies the significant factors that affect the industry’s pricing behavior. In the second step, a non-parametric regression of price on the SIR factors estimates the link functions. The output of the algorithm offers useful information to identify the model that best approximates the industry’s pricing behavior. In addition, the output of the algorithm is used to develop a post-estimation test for model specification. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Review of Industrial Organization Springer Journals

Estimating Market Power with Weak A Priori Information: An Exploratory Approach to the Model-Specification Problem

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Publisher
Springer US
Copyright
Copyright © 2012 by Springer Science+Business Media, LLC.
Subject
Economics; Industrial Organization; Microeconomics
ISSN
0889-938X
eISSN
1573-7160
D.O.I.
10.1007/s11151-012-9333-0
Publisher site
See Article on Publisher Site

Abstract

Choosing the wrong theoretical model to describe an industry’s behavior may lead to biased estimates of the degree of market power. This paper presents a two-step, data-driven methodology to reduce the risk of mis-specification bias. In the first step, a sliced inverse regression identifies the significant factors that affect the industry’s pricing behavior. In the second step, a non-parametric regression of price on the SIR factors estimates the link functions. The output of the algorithm offers useful information to identify the model that best approximates the industry’s pricing behavior. In addition, the output of the algorithm is used to develop a post-estimation test for model specification.

Journal

Review of Industrial OrganizationSpringer Journals

Published: Jan 17, 2012

References

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