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dc.contributor.authorCaleiro, Antónioen
dc.date.accessioned2009-01-28T15:05:24Z-
dc.date.available2009-01-28T15:05:24Z-
dc.date.issued2008-
dc.identifier.urihttp://hdl.handle.net/10419/17988-
dc.description.abstractThe literature on electoral cycles has developed in two distinct phases. The first one considered the existence of non-rational (naive) voters whereas the second one considered fully rational voters. In our perspective, an intermediate approach is more interesting, i.e. one that considers learning voters, which are boundedly rational. In this sense, neural networks may be considered as learning mechanisms used by voters to perform a classification of the incumbent in order to distinguish opportunistic (electorally motivated) from benevolent (non-electorally motivated) behaviour. The paper shows in which circumstances a neural network, namely a perceptron, can resolve that problem of classification. This is done by considering a model allowing for output persistence, which is a feature of aggregate supply that, indeed, may make it impossible to correctly classify the incumbent.en
dc.language.isoengen
dc.publisher|aKiel Institute for the World Economy (IfW) |cKielen
dc.relation.ispartofseries|aEconomics Discussion Papers |x2008-16en
dc.subject.jelE32en
dc.subject.jelD72en
dc.subject.jelC45en
dc.subject.ddc330en
dc.subject.keywordClassificationen
dc.subject.keywordelectionsen
dc.subject.keywordincumbenten
dc.subject.keywordneural networksen
dc.subject.keywordoutputen
dc.subject.keywordpersistenceen
dc.subject.keywordperceptronsen
dc.subject.stwPolitischer Konjunkturzyklusen
dc.subject.stwWahlverhaltenen
dc.subject.stwLernprozessen
dc.subject.stwNeuronale Netzeen
dc.subject.stwTheorieen
dc.titleHow Can Voters Classify an Incumbent under Output Persistence-
dc.typeWorking Paperen
dc.identifier.ppn561923051en
dc.rights.licensehttp://creativecommons.org/licenses/by-nc/2.0/de/deed.enen
dc.identifier.repecRePEc:zbw:ifwedp:7258en

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