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How to Improve the SPF Forecasts?

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  • Mihaela Bratu (Simionescu)

    (Academy of Economic Studies, Bucharest, Romania)

Abstract

Many researchers are interested in making predictions for macroeconomic variables, but few of them studied the accuracy of their forecasts. The problem is essential, especially in crisis periods, because from many forecasts made for the same indicator only one or few are the most accurate. In this research, some alternative forecasts for the annual rate of change for the HICP for EU were developed. Their accuracy was evaluated and compared with the accuracy of SPF predictions. All the proposed predictions for January 2010-May 2012 (those based on a random walk developed for 1997-2009, combined forecasts, the median and the mean of forecasts, predictions based on different econometric models that take into account the previous SPF forecasts) were not more accurate than the naïve forecasts or SPF ones. A considerably improvement of the accuracy was gotten for predictions based on mean error of SPF expectations for 1997-2009 and the previous registered value. This empirical strategy of building more accurate forecasts was better than the classical theoretical approaches from literature, but it is still less accurate than the naïve forecasts that could be made for UE inflation rate. So, the forecasts based on a simple econometric model as the random walk from the naïve approach are the most accurate, conclusion that is in accordance with the latest researches in literature and with one of the essential condition in forecasting theory.

Suggested Citation

  • Mihaela Bratu (Simionescu), 2013. "How to Improve the SPF Forecasts?," Acta Universitatis Danubius. OEconomica, Danubius University of Galati, issue 9(2), pages 153-165, April.
  • Handle: RePEc:dug:actaec:y:2013:i:2:p:153-165
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    References listed on IDEAS

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    1. Dovern, Jonas & Weisser, Johannes, 2011. "Accuracy, unbiasedness and efficiency of professional macroeconomic forecasts: An empirical comparison for the G7," International Journal of Forecasting, Elsevier, vol. 27(2), pages 452-465.
    2. Heilemann, Ullrich & Stekler, Herman, 2007. "Introduction to "The future of macroeconomic forecasting"," International Journal of Forecasting, Elsevier, vol. 23(2), pages 159-165.
    3. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    4. Clements, Michael P & Hendry, David F, 1995. "Forecasting in Cointegration Systems," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 10(2), pages 127-146, April-Jun.
    5. Granger, Clive W. J. & Jeon, Yongil, 2003. "Comparing forecasts of inflation using time distance," International Journal of Forecasting, Elsevier, vol. 19(3), pages 339-349.
    6. Francis X. Diebold, 1998. "The Past, Present, and Future of Macroeconomic Forecasting," Journal of Economic Perspectives, American Economic Association, vol. 12(2), pages 175-192, Spring.
    7. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    8. Terasvirta, Timo & van Dijk, Dick & Medeiros, Marcelo C., 2005. "Linear models, smooth transition autoregressions, and neural networks for forecasting macroeconomic time series: A re-examination," International Journal of Forecasting, Elsevier, vol. 21(4), pages 755-774.
    9. Ruth, Karsten, 2008. "Macroeconomic forecasting in the EMU: Does disaggregate modeling improve forecast accuracy?," Journal of Policy Modeling, Elsevier, vol. 30(3), pages 417-429.
    10. Gorr, Wilpen L., 2009. "Forecast accuracy measures for exception reporting using receiver operating characteristic curves," International Journal of Forecasting, Elsevier, vol. 25(1), pages 48-61.
    11. Ashley, Richard, 2003. "Statistically significant forecasting improvements: how much out-of-sample data is likely necessary?," International Journal of Forecasting, Elsevier, vol. 19(2), pages 229-239.
    12. Dobrescu, Emilian, 2003. "Macroeconomic Estimations For The Romanian “Pre-Accession Economic Program” (The 2003 Version)," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 23-49, December.
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