Record Details

Replication Data for: Unifying the Study of Asymmetric Hypotheses

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

View Archive Info
 
 
Field Value
 
Title Replication Data for: Unifying the Study of Asymmetric Hypotheses
 
Identifier https://doi.org/10.7910/DVN/9NXGHP
 
Creator Rosenberg, Andrew S.
Knuppe, Austin J.
Braumoeller, Bear F.
 
Publisher Harvard Dataverse
 
Description This is a replication folder for “Unifying the Study of Asymmetric Hypotheses."

This article presents a conceptual clarification of asymmetric hypotheses and a discussion of methodologies available to test them. Despite the existence of a litany of theories that posit asymmetric hypotheses, most empirical studies fail to capture their core insight: boundaries separating zones of data from areas that lack data are substantively interesting. We discuss existing set-theoretic and large-N approaches to the study of asymmetric hy- potheses, introduce new ones from the literatures on stochastic frontier and data envelopment analysis, evaluate their relative merits, and give three examples of how asymmetric hypotheses can be studied with this suite of tools.
 
Subject Social Sciences
Asymmetric hypotheses
Causal complexity
Boundary analysis
Data envelopment
 
Contributor Rosenberg, Andrew