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dc.contributor.advisorMcCracken, Michael W.eng
dc.contributor.authorSweet, Dustin L.eng
dc.date.issued2005eng
dc.date.submitted2005 Springeng
dc.descriptionThe entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file.eng
dc.descriptionTitle from title screen of research.pdf file viewed on (July 11, 2006)eng
dc.descriptionIncludes bibliographical references.eng
dc.descriptionThesis (M.S.) University of Missouri-Columbia 2005.eng
dc.descriptionDissertations, Academic -- University of Missouri--Columbia -- Economics.eng
dc.description.abstractUsing regional data from Missouri, I compare the forecasting performance of a univariate autoregressive model to another which considers spatial correlation. I build each type of model for each designated area and evaluate their performance in making unemployment predictions in both a one-month and one-year horizon. For both specifications, BIC is used to determine the most effective model. After discovering the best model for each region in both univariate and spatial frameworks, I use both to make unemployment forecasts. Using mean square forecast error as the guide for accuracy, I find that in the shorter horizon the AR model is more effective while the spatial model is a better predictor in the one-year forecasts.eng
dc.identifier.merlinb55874599eng
dc.identifier.urihttp://hdl.handle.net/10355/4250
dc.languageEnglisheng
dc.publisherUniversity of Missouri--Columbiaeng
dc.relation.ispartofcommunityUniversity of Missouri--Columbia. Graduate School. Theses and Dissertationseng
dc.subject.lcshUnemployment -- Forecastingeng
dc.titleForecasting unemployment with spatial correlationeng
dc.typeThesiseng
thesis.degree.disciplineEconomics (MU)eng
thesis.degree.grantorUniversity of Missouri--Columbiaeng
thesis.degree.levelMasterseng
thesis.degree.nameM.S.eng


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