What Can We Learn from Predictive Modeling?

Skyler J. Cranmer and Bruce A. Desmarais

Political Analysis, 2017, vol. 25, issue 2, 145-166

Abstract: The large majority of inferences drawn in empirical political research follow from model-based associations (e.g., regression). Here, we articulate the benefits of predictive modeling as a complement to this approach. Predictive models aim to specify a probabilistic model that provides a good fit to testing data that were not used to estimate the model’s parameters. Our goals are threefold. First, we review the central benefits of this under-utilized approach from a perspective uncommon in the existing literature: we focus on how predictive modeling can be used to complement and augment standard associational analyses. Second, we advance the state of the literature by laying out a simple set of benchmark predictive criteria. Third, we illustrate our approach through a detailed application to the prediction of interstate conflict.

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:cup:polals:v:25:y:2017:i:02:p:145-166_00

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