Enriching interactions: Incorporating outcome data into static discrete games |
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Authors: | Paul B Ellickson Sanjog Misra |
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Institution: | (1) William E. Simon School of Business Administration, University of Rochester, Rochester, NY 14627, USA |
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Abstract: | When modeling the behavior of firms, marketers and micro-economists routinely confront complex problems of strategic interaction.
In competitive environments, firms make strategic decisions that not only depend on the features of the market, but also on
their beliefs regarding the reactions of their rivals. Structurally modeling these interactions requires formulating and estimating
a discrete game, a task which, until recently, was considered intractable. Fortunately, two-step estimation methods have cracked
the problem, fueling a growing literature in both marketing and economics that tackles a host of issues from the optimal design
of ATM networks to the choice of pricing strategy. However, most existing methods have focused on only the discrete choice
of actions, ignoring a wealth of information contained in post-choice outcome data and severely limiting the scope for performing
informative counterfactuals or identifying the deep structural parameters that drive strategic decisions. The goal of this
paper is to provide a method for incorporating post-choice outcome data into static discrete games of incomplete information.
In particular, our estimation approach adds a selection correction to the two-step games approach, allowing the researcher
to use revenue data, for example, to recover the costs associated with alternative actions. Alternatively, a researcher might
use R&D expenses to back out the returns to innovation. |
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Keywords: | |
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