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Forecasting annual inflation with power transformations: the case of inflation targeting countries

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  • Héctor Manuel Záarte Solano
  • Angélica Rengifo Gómez

Abstract

This paper investigates whether transforming the Consumer Price Index with a class of power transformations lead to an improvement of inflation forecasting accuracy. We use one of the prototypical models to forecast short run inflation which is known as the univariate time series ARIMA . This model is based on past inflation which is traditionally approximated by the difference of logarithms of the underlying consumer price index. The common practice of applying the logarithm could damage the forecast precision if this transformation does not stabilize the variance adequately. In this paper we investigate the benefits of incorporating these transformations using a sample of 28 countries that has adopted the inflation targeting framework. An appropriate transformation reduces problems with estimation, prediction and inference. The choice of the parameter is done by bayesian grounds.

Suggested Citation

  • Héctor Manuel Záarte Solano & Angélica Rengifo Gómez, 2013. "Forecasting annual inflation with power transformations: the case of inflation targeting countries," Borradores de Economia 10462, Banco de la Republica.
    • Handle: RePEc:col:000094:010462
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    References listed on IDEAS

    as
    1. Philip Hans Franses & Michael McAleer, 1998. "Testing for Unit Roots and Non‐linear Transformations," Journal of Time Series Analysis, Wiley Blackwell, vol. 19(2), pages 147-164, March.
    2. Luetkepohl Helmut & Xu Fang, 2011. "Forecasting Annual Inflation with Seasonal Monthly Data: Using Levels versus Logs of the Underlying Price Index," Journal of Time Series Econometrics, De Gruyter, vol. 3(1), pages 1-23, February.
    3. Stock, James H. & Watson, Mark W., 1999. "Forecasting inflation," Journal of Monetary Economics, Elsevier, vol. 44(2), pages 293-335, October.
    4. 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.
    5. Hosoya, Yuzo & Terasaka, Takahiro, 2009. "Inference on transformed stationary time series," Journal of Econometrics, Elsevier, vol. 151(2), pages 129-139, August.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    ARIMA models; power transformations; seasonality; bayesian analysis.;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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