Testing for stochastic dominance in social networks

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dc.contributor.authorDoko Tchatoka, F.
dc.contributor.authorGarrard, R.
dc.contributor.authorMasson, V.
dc.date.issued2017
dc.descriptionAlso can be found at https://ideas.repec.org/p/adl/wpaper/2017-02.html
dc.description.abstractThis paper illustrates how stochastic dominance criteria can be used to rank social networks in terms of efficiency, and develops statistical inference procedures for as- sessing these criteria. The tests proposed can be viewed as extensions of a Pearson goodness-of-fit test and a studentized maximum modulus test often used to partially rank income distributions and inequality measures. We establish uniform convergence of the empirical size of the tests to the nominal level, and show their consistency under the usual conditions that guarantee the validity of the approximation of a multinomial distribution to a Gaussian distribution. Furthermore, we propose a bootstrap method that enhances the finite-sample properties of the tests. The performance of the tests is illustrated via Monte Carlo experiments and an empirical application to risk sharing networks in rural India
dc.description.statementofresponsibilityFirmin Doko Tchatoka, Robert Garrard and Virginie Masson
dc.identifier.orcidDoko Tchatoka, F. [0000-0003-1876-0633]
dc.identifier.orcidMasson, V. [0000-0002-3597-3019]
dc.identifier.urihttp://hdl.handle.net/2440/106215
dc.language.isoen
dc.publisherUniversity of Adelaide, School of Economics
dc.relation.ispartofseriesSchool of Economics Working Papers
dc.rightsCopyright the authors
dc.source.urihttp://www.economics.adelaide.edu.au/research/papers/
dc.subjectNetworks; Tests of stochastic dominance; Bootstrap; Uniform convergence
dc.titleTesting for stochastic dominance in social networks
dc.typeWorking paper
pubs.publication-statusPublished

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