Forecasting Annual Inflation with Seasonal Monthly Data: Using Levels versus Logs of the Underlying Price Index

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dc.contributor.author LUETKEPOHL, Helmut
dc.contributor.author XU, Fang
dc.date.accessioned 2009-11-06T12:15:03Z
dc.date.available 2009-11-06T12:15:03Z
dc.date.issued 2009
dc.identifier.issn 1830-7728
dc.identifier.uri http://hdl.handle.net/1814/12779
dc.description.abstract This 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.language.iso en en
dc.relation.ispartofseries EUI MWP en
dc.relation.ispartofseries 2009/37 en
dc.subject C22 en
dc.subject Autoregressive moving average process en
dc.subject forecast mean squared error en
dc.subject log transformation en
dc.subject seasonally integrated process en
dc.subject seasonal dummy variables en
dc.title Forecasting Annual Inflation with Seasonal Monthly Data: Using Levels versus Logs of the Underlying Price Index en
dc.type Working Paper en
dc.neeo.contributor LUETKEPOHL|Helmut|aut|EUI70007
dc.neeo.contributor XU|Fang|aut|
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