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.
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Bibliographic InfoPaper provided by BANCO DE LA REPÚBLICA in its series BORRADORES DE ECONOMIA with number 010462.
Date of creation: 05 Feb 2013
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ARIMA models; power transformations; seasonality; bayesian analysis.;
Find related papers by JEL classification:
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
This paper has been announced in the following NEP Reports:
- NEP-ALL-2013-02-16 (All new papers)
- NEP-CBA-2013-02-16 (Central Banking)
- NEP-FOR-2013-02-16 (Forecasting)
- NEP-MAC-2013-02-16 (Macroeconomics)
- NEP-MON-2013-02-16 (Monetary Economics)
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.:
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1 OMEWP, University of Sydney Business School, Discipline of Business Analytics.
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