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Forecasting annual inflation with seasonal monthly data: using levels versus logs of the underlying price index

Luetkepohl, H. and Xu, F. (2011) Forecasting annual inflation with seasonal monthly data: using levels versus logs of the underlying price index. Journal of Time Series Econometrics, 3 (1). pp. 1-21. ISSN 1941-1928

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To link to this article DOI: 10.2202/1941-1928.1094

Abstract/Summary

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.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Arts, Humanities and Social Science > School of Politics, Economics and International Relations > Economics
ID Code:26589
Publisher:De Gruyter

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