Forecasting annual inflation with power transformations: the case of inflation targeting countries
AbstractThis 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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by Banco de la Republica de Colombia in its series Borradores de Economia with number 756.
Date of creation: Feb 2013
Date of revision:
ARIMA models; power transformations; seasonality; bayesian analysis. Classification JEL:C22; C52;
Find related papers by JEL classification:
- bay - - - - - -
- ana - - - - - -
- Cla - Mathematical and Quantitative Methods - - - - -
- JEL - Labor and Demographic Economics - - - - -
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
This paper has been announced in the following NEP Reports:
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Stock, James H. & Watson, Mark W., 1999.
Journal of Monetary Economics,
Elsevier, vol. 44(2), pages 293-335, October.
- Hosoya, Yuzo & Terasaka, Takahiro, 2009. "Inference on transformed stationary time series," Journal of Econometrics, Elsevier, vol. 151(2), pages 129-139, August.
- Tommaso, Proietti & Helmut, Luetkepohl, 2011.
"Does the Box-Cox transformation help in forecasting macroeconomic time series?,"
32294, University Library of Munich, Germany.
- 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.
- Tommaso Proietti & Helmut Luetkepohl, 2011. "Does the Box-Cox Transformation Help in Forecasting Macroeconomic Time Series?," Economics Working Papers ECO2011/29, European University Institute.
- 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.
- repec:syb:wpbsba:08/2011 is not listed on IDEAS
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Camilo Millán).
If references are entirely missing, you can add them using this form.