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

EUI Max Weber Programme Working Paper No. 2009/37

32 Pages Posted: 14 Nov 2009

See all articles by Helmut Luetkepohl

Helmut Luetkepohl

European University Institute; CESifo (Center for Economic Studies and Ifo Institute)

Fang Xu

European University Institute; Christian-Albrechts-University of Kiel

Date Written: November 1, 2009

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.

Keywords: Autoregressive moving average process, forecast mean squared error, log transformation, seasonally integrated process, seasonal dummy variables

JEL Classification: C22

Suggested Citation

Luetkepohl, Helmut and Xu, Fang, Forecasting Annual Inflation with Seasonal Monthly Data: Using Levels Versus Logs of the Underlying Price Index (November 1, 2009). EUI Max Weber Programme Working Paper No. 2009/37, Available at SSRN: https://ssrn.com/abstract=1505472 or http://dx.doi.org/10.2139/ssrn.1505472

Helmut Luetkepohl

European University Institute ( email )

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CESifo (Center for Economic Studies and Ifo Institute)

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Germany

Fang Xu (Contact Author)

European University Institute ( email )

Max Weber Programme
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Italy

Christian-Albrechts-University of Kiel ( email )

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Kiel, 24118
Germany

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