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

  • Hector Manuel Zárate Solano

    ()

  • Angélica Rengifo Gómez

    ()

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.

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Paper provided by Banco de la Republica de Colombia in its series Borradores de Economia with number 756.

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Length: 12
Date of creation: Feb 2013
Date of revision:
Handle: RePEc:bdr:borrec:756
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  1. Lütkepohl, Helmut & Proietti, Tommaso, 2011. "Does the Box-Cox transformation help in forecasting macroeconomic time series?," Working Papers 1 OMEWP, University of Sydney Business School, Discipline of Business Analytics.
  2. Hosoya, Yuzo & Terasaka, Takahiro, 2009. "Inference on transformed stationary time series," Journal of Econometrics, Elsevier, vol. 151(2), pages 129-139, August.
  3. James H. Stock & Mark W. Watson, 1999. "Forecasting Inflation," NBER Working Papers 7023, National Bureau of Economic Research, Inc.
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