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New accuracy measures for point and interval forecasts. A case study for Romania’s forecasts of inflation and unemployment rate

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  • Mihaela Bratu

    (Academia de Estudios Económicos de Bucarest.)

Abstract

The objective of this research is to introduce in literature new measures of accuracy for point forecasts (radical of order n of the mean of squared errors, mean for the difference between each predicted value and the mean of the effective values, ratio of radicals of sum of squared errors (RRSSE), the last one being used for forecasts comparisons), different versions of U2 Theil’s statistic) and for forecast intervals (number of intervals including the realization, difference between the realization and the lower limit, the upper one, respectively the interval centre). Some classical measures of predictions accuracy were assessed for the inflation and unemployment rate forecasts provided for Romania by Institute for Economic Forecasting (IEF) and National Commission of Prognosis (NCP) on the horizon 2010-2012. Excepting the best forecast, the hierarchy of predictions provided by the classical indicators and by the new ones are different. A novelty in literature is also brought by the methods of building the forecasts intervals. The classical interval based on the root mean squared error method was adapted to the small sample of forecasts. The intervals based on the standard deviation and those constructed using bootstrap technique and bias-corrected-accelerated (BCA) bootstrap method are proposed as an original way in this field.

Suggested Citation

  • Mihaela Bratu, 2013. "New accuracy measures for point and interval forecasts. A case study for Romania’s forecasts of inflation and unemployment rate," Economic Analysis Working Papers (2002-2010). Atlantic Review of Economics (2011-2016), Colexio de Economistas de A Coruña, Spain and Fundación Una Galicia Moderna, vol. 1, pages 1-1, June.
    • Handle: RePEc:eac:articl:04/12
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    References listed on IDEAS

    as
    1. Ippei Fujiwara & Maiko Koga, 2002. "A Statistical Forecasting Method for Inflation Forecasting," Bank of Japan Working Paper Series Research and Statistics D, Bank of Japan.
    2. Fujiwara, Ippei & Koga, Maiko, 2004. "A Statistical Forecasting Method for Inflation Forecasting: Hitting Every Vector Autoregression and Forecasting under Model Uncertainty," Monetary and Economic Studies, Institute for Monetary and Economic Studies, Bank of Japan, vol. 22(1), pages 123-142, March.
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