Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/17988
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dc.contributor.authorCaleiro, Antónioen_US
dc.date.accessioned2009-01-28T15:05:24Z-
dc.date.available2009-01-28T15:05:24Z-
dc.date.issued2008en_US
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_US
dc.language.isoengen_US
dc.publisher|aKiel Institute for the World Economy (IfW) |cKielen_US
dc.relation.ispartofseries|aEconomics Discussion Papers / Institut für Weltwirtschaft |x2008-16en_US
dc.subject.jelE32en_US
dc.subject.jelD72en_US
dc.subject.jelC45en_US
dc.subject.ddc330en_US
dc.subject.keywordClassificationen_US
dc.subject.keywordelectionsen_US
dc.subject.keywordincumbenten_US
dc.subject.keywordneural networksen_US
dc.subject.keywordoutputen_US
dc.subject.keywordpersistenceen_US
dc.subject.keywordperceptronsen_US
dc.subject.stwPolitischer Konjunkturzyklusen_US
dc.subject.stwWahlverhaltenen_US
dc.subject.stwLernprozessen_US
dc.subject.stwNeuronale Netzeen_US
dc.subject.stwTheorieen_US
dc.titleHow Can Voters Classify an Incumbent under Output Persistenceen_US
dc.typeWorking Paperen_US
dc.identifier.ppn561923051en_US
dc.rights.licensehttp://creativecommons.org/licenses/by-nc/2.0/de/deed.en-
dc.identifier.repecRePEc:zbw:ifwedp:7258-

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