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Two Quantitative Forecasting Methods For Macroeconomic Indicators In Czech Republic

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  • BRATU SIMIONESCU, Mihaela

    ()
    (Academy of Economic Studies)

Abstract

Econometric modelling and exponential smoothing techniques are two quantitative forecasting methods with good results in practice, but the objective of the research was to find out which of the two techniques are better for short run predictions. Therefore, for inflation, unemployment and interest rate in Czech Republic some accuracy indicators were calculated for the predictions based on these methods. Short run forecasts on a horizon of 3 months were made for December 2011-February 2012, the econometric models being updated. For Czech Republic, the exponential smoothing techniques provided more accurate forecasts than the econometric models (VAR(2) models, ARMA procedure and models with lagged variables). One explication for the better performance of smoothing techniques would be that in the chosen countries the short run predictions more influenced by the recent evolution of the indicators.

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Bibliographic Info

Article provided by Universitatea Spiru Haret in its journal Annals of Spiru Haret University, Economic Series.

Volume (Year): 3 (2012)
Issue (Month): 1 ()
Pages: 71-87

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Handle: RePEc:ris:sphecs:0165

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Related research

Keywords: accuracy; econometric models; forecasts; forecasting methods; smoothing exponential techniques;

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References

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  1. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-44, January.
  2. 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.
  3. 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.
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  5. 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.
  6. George Athanasopoulos & Farshid Vahid, 2006. "A Complete VARMA Modelling Methodology Based on Scalar Components," Monash Econometrics and Business Statistics Working Papers 2/06, Monash University, Department of Econometrics and Business Statistics.
  7. Debby Lanser & Henk Kranendonk, 2008. "Investigating uncertainty in macroeconomic forecasts by stochastic simulation," CPB Discussion Paper 112, CPB Netherlands Bureau for Economic Policy Analysis.
  8. Francis X. Diebold, 1997. "The past, present, and future of macroeconomic forecasting," Working Papers 97-20, Federal Reserve Bank of Philadelphia.
  9. 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.
  10. Harvey, David I. & Newbold, Paul, 2003. "The non-normality of some macroeconomic forecast errors," International Journal of Forecasting, Elsevier, vol. 19(4), pages 635-653.
  11. 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.
  12. Heilemann, Ullrich & Stekler, Herman, 2007. "Introduction to "The future of macroeconomic forecasting"," International Journal of Forecasting, Elsevier, vol. 23(2), pages 159-165.
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