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The Assessment And Improvement Of The Accuracy For The Forecast Intervals

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  • Bratu, Mihaela

    () (Academy of Economic Studies. Faculty of Cybernetics, Statistics and Economic Informatics.)

Abstract

The objective of this research is to present some accuracy measures associated to forecast intervals, taken into account the fact that in literature some specific accuracy indicators for this type of prediction have not been proposed yet. For the quarterly inflation rate provided by the National Bank of Romania, forecast intervals were built on the horizon 2010-2012. According to the number of intervals that include the real value and to an econometric procedure based on DUMMY variables, the intervals based on historical errors (RMSE- root mean squared errors) are better than those based on BCA bootstrap procedure. However, the new indicator proposed in this paper as a measure of global accuracy, M indicator, the forecast intervals based on BCA bootstraping are more accurate than the intervals based on historical RMSE. Bayesian intervals were constructed for quarterly USA inflation in 2012 using aprioristic information, but the smaller intervals did not imply an increase in the degree of accuracy.

Suggested Citation

  • Bratu, Mihaela, 2013. "The Assessment And Improvement Of The Accuracy For The Forecast Intervals," Working Papers of Macroeconomic Modelling Seminar 132602, Institute for Economic Forecasting.
  • Handle: RePEc:rjr:wpmems:132602
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    References listed on IDEAS

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    1. Henning Fischer & Marta García-Bárzana & Peter Tillmann & Peter Winker, 2014. "Evaluating FOMC forecast ranges: an interval data approach," Empirical Economics, Springer, vol. 47(1), pages 365-388, August.
    2. Borbély, Dóra & Meier, Carsten-Patrick, 2003. "Macroeconomic interval forecasting: the case of assessing the risk of deflation in Germany," Kiel Working Papers 1153, Kiel Institute for the World Economy (IfW).
    3. Chatfield, Chris, 1993. "Calculating Interval Forecasts: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(2), pages 143-144, April.
    4. Jian Wang & Jason J. Wu, 2012. "The Taylor Rule and Forecast Intervals for Exchange Rates," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 44(1), pages 103-144, February.
    5. Chatfield, Chris, 1993. "Calculating Interval Forecasts," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(2), pages 121-135, April.
    6. Peter Tulip & Stephanie Wallace, 2012. "Estimates of Uncertainty around the RBA's Forecasts," RBA Research Discussion Papers rdp2012-07, Reserve Bank of Australia.
    7. Knüppel, Malte & Schultefrankenfeld, Guido, 2008. "How informative are macroeconomic risk forecasts? An examination of the Bank of England's inflation forecasts," Discussion Paper Series 1: Economic Studies 2008,14, Deutsche Bundesbank.
    8. Matei Demetrescu, 2007. "Optimal forecast intervals under asymmetric loss," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(4), pages 227-238.
    9. Diebold, Francis X & West, Kenneth D, 1998. "Symposium on Forecasting and Empirical Methods in Macroeconomics and Finance: Editors' Introduction," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 811-815, November.
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    More about this item

    Keywords

    forecast intervals; accuracy; uncertainty; BCA bootstrap intervals; indicator M;

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • L6 - Industrial Organization - - Industry Studies: Manufacturing

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