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

Luetkepohl Helmut and Fang Xu
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Luetkepohl Helmut: European University Institute
Authors registered in the RePEc Author Service: Helmut Lütkepohl

Journal of Time Series Econometrics, 2011, vol. 3, issue 1, 23

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; mean squared error; log transformation; seasonal dummy variables (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (11)

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DOI: 10.2202/1941-1928.1094

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