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Replication Data for: Tree-Based Models for Political Science Data

Harvard Dataverse (Africa Rice Center, Bioversity International, CCAFS, CIAT, IFPRI, IRRI and WorldFish)

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Title Replication Data for: Tree-Based Models for Political Science Data
 
Identifier https://doi.org/10.7910/DVN/8ZJBLI
 
Creator Montgomery, Jacob M.
Olivella, Santiago
 
Publisher Harvard Dataverse
 
Description Political scientists often find themselves analyzing datasets with a large number of observations, a large number of variables, or both. Yet, traditional statistical techniques fail to take full advantage of the opportunities inherent in ``big data'' as they are too rigid to recover nonlinearities and do not facilitate the easy exploration of interactions in high-dimensional datasets. In this paper, we introduce a family of tree-based nonparametric techniques that may, in some circumstances, be more appropriate than traditional methods for confronting these data challenges. In particular, tree models are very effective for detecting nonlinearities and interactions, even in datasets with many (potentially irrelevant) covariates. We introduce the basic logic of tree-based models, provide an overview of the most prominent methods in the literature, and conduct three analyses that illustrate how the methods can be implemented while highlighting both their advantages and limitations.
 
Subject Social Sciences
Classification and regression trees
 
Contributor Olivella, Santiago