Stochastic Process Methods with an Application to Budgetary Data

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BREUNIG, Christian, Bryan D. JONES, 2010. Stochastic Process Methods with an Application to Budgetary Data. In: Political Analysis. 19(1), pp. 103-117. ISSN 1047-1987. eISSN 1476-4989

@article{Breunig2010Stoch-23161, title={Stochastic Process Methods with an Application to Budgetary Data}, year={2010}, doi={10.1093/pan/mpq038}, number={1}, volume={19}, issn={1047-1987}, journal={Political Analysis}, pages={103--117}, author={Breunig, Christian and Jones, Bryan D.} }

<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:bibo="http://purl.org/ontology/bibo/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcterms="http://purl.org/dc/terms/" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > <rdf:Description rdf:about="https://kops.uni-konstanz.de/rdf/resource/123456789/23161"> <dc:contributor>Breunig, Christian</dc:contributor> <dcterms:bibliographicCitation>Political Analysis ; 19 (2011), 1. - S. 103-117</dcterms:bibliographicCitation> <dcterms:issued>2010</dcterms:issued> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2013-05-21T08:47:44Z</dcterms:available> <dc:contributor>Jones, Bryan D.</dc:contributor> <dcterms:rights rdf:resource="http://nbn-resolving.org/urn:nbn:de:bsz:352-20140905103605204-4002607-1"/> <bibo:uri rdf:resource="http://kops.uni-konstanz.de/handle/123456789/23161"/> <dc:creator>Jones, Bryan D.</dc:creator> <dc:creator>Breunig, Christian</dc:creator> <dcterms:abstract xml:lang="eng">Political scientists have increasingly focused on causal processes that operate not solely on mean differences but on other stochastic characteristics of the distribution of a dependent variable. This paper surveys important statistical tools used to assess data in situations where the entire distribution of values is of interest. We first outline three broad conditions under which stochastic process methods are applicable and show that these conditions cover many domains of social inquiry. We discuss a variety of visual and analytical techniques, including distributional analysis, direct parameter estimates of probability density functions, and quantile regression. We illustrate the utility of these statistical tools with an application to budgetary data because strong theoretical expectations at the micro- and macrolevel exist about the distributional characteristics for such data. The expository analysis concentrates on three budget series (total, domestic, and defense outlays) of the U.S. government for 1800–2004.</dcterms:abstract> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2013-05-21T08:47:44Z</dc:date> <dc:language>eng</dc:language> <dcterms:title>Stochastic Process Methods with an Application to Budgetary Data</dcterms:title> <dc:rights>deposit-license</dc:rights> </rdf:Description> </rdf:RDF>

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