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dc.contributor.authorHubrich, Kirstinen
dc.contributor.authorWest, Kennethen
dc.date.accessioned2010-08-11T09:02:14Z-
dc.date.available2010-08-11T09:02:14Z-
dc.date.issued2010-
dc.identifier.urihttp://hdl.handle.net/10419/37160-
dc.description.abstractWe propose two new procedures for comparing the mean squared prediction error (MSPE) of a benchmark model to the MSPEs of a small set of alternative models that nest the benchmark. Our procedures compare the benchmark to all the alternative models simultaneously rather than sequentially, and do not require reestimation of models as part of a bootstrap procedure. Both procedures adjust MSPE differences in accordance with Clark and West (2007); one procedure then examines the maximum tstatistic, the other computes a chi-squared statistic. Our simulations examine the proposed procedures and two existing procedures that do not adjust the MSPE differences: a chi-squared statistic, and White's (2000) reality check. In these simulations, the two statistics that adjust MSPE differences have most accurate size, and the procedure that looks at the maximum t-statistic has best power. We illustrate our procedures by comparing forecasts of different models for U.S. inflation.en
dc.language.isoengen
dc.publisher|aVerein für Socialpolitik |cFrankfurt a. M.en
dc.relation.ispartofseries|aBeiträge zur Jahrestagung des Vereins für Socialpolitik 2010: Ökonomie der Familie - Session: Macroeconomic Modeling and Forecasting Performance |xD11-V3en
dc.subject.jelC53en
dc.subject.jelC52en
dc.subject.jelE31en
dc.subject.ddc330en
dc.subject.keywordOut-of-sampleen
dc.subject.keywordpredictionen
dc.subject.keywordtestingen
dc.subject.keywordmultiple model comparisonsen
dc.subject.keywordinflation forecastingen
dc.titleForecast Evaluation of Small Nested Model Sets-
dc.typeConference Paperen
dc.identifier.ppn655604537en
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen

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