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

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Author Info

  • Luetkepohl Helmut

    (European University Institute)

  • Xu Fang

    (European University Institute and Christian-Albrechts-Universität zu Kiel)

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.

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Bibliographic Info

Article provided by De Gruyter in its journal Journal of Time Series Econometrics.

Volume (Year): 3 (2011)
Issue (Month): 1 (February)
Pages: 1-23

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Handle: RePEc:bpj:jtsmet:v:3:y:2011:i:1:n:7

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Cited by:
  1. Proietti, Tommaso & Lütkepohl, Helmut, 2013. "Does the Box–Cox transformation help in forecasting macroeconomic time series?," International Journal of Forecasting, Elsevier, vol. 29(1), pages 88-99.
  2. Santiago Cajiao Raigosa & Luis Fernando Melo Velandia & Daniel Parra Amado, 2014. "Pronósticos para una economía menos volátil: El caso colombiano," Borradores de Economia 821, Banco de la Republica de Colombia.

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