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dc.contributor.authorLUETKEPOHL, Helmut
dc.contributor.authorXU, Fang
dc.date.accessioned2009-11-06T12:15:03Z
dc.date.available2009-11-06T12:15:03Z
dc.date.issued2009
dc.identifier.issn1830-7728
dc.identifier.urihttp://hdl.handle.net/1814/12779
dc.description.abstractThis paper investigates whether using natural logarithms (logs) of price indices for forecasting inflation rates is preferable to employing the original series. Univariate forecasts for annual inflation rates for a number of European countries and the USA based on monthly seasonal consumer price indices are considered. Stochastic seasonality and deterministic seasonality models are used. In many cases the forecasts based on the original variables result in substantially smaller root mean squared errors than models based on logs. In turn, if forecasts based on logs are superior, the gains are typically small. This outcome sheds doubt on the common practice in the academic literature to forecast inflation rates based on differences of logs.en
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.relation.ispartofseriesEUI MWPen
dc.relation.ispartofseries2009/37en
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectC22en
dc.subjectAutoregressive moving average processen
dc.subjectforecast mean squared erroren
dc.subjectlog transformationen
dc.subjectseasonally integrated processen
dc.subjectseasonal dummy variablesen
dc.titleForecasting Annual Inflation with Seasonal Monthly Data: Using Levels versus Logs of the Underlying Price Indexen
dc.typeWorking Paperen
dc.neeo.contributorLUETKEPOHL|Helmut|aut|EUI70007
dc.neeo.contributorXU|Fang|aut|
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