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Incorporating uncertainties into economic forecasts: an application to forecasting economic activity in Croatia

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Author Info

  • Dario Rukelj

    (Ministry of Finance of the Republic of Croatia, Zagreb)

  • Barbara Ulloa

    (Cass Business School, London)

Abstract

In this paper we present a framework for incorporating uncertainties into economic activity forecasts for Croatia. Using the vector error correction model (VECM) proposed by Rukelj (2010) as the benchmark model, we forecast densities of the variable of interest using stochastic simulations for incorporating future and parameter uncertainty. We exploit the use of parametric and non-parametric approaches in generating random shocks as in Garrat et al. (2003). Finally we evaluate the results by the Kolmogorov-Smirnov and Anderson-Darling test of probability integral transforms. The main fi ndings are: (1) the parametric and the non-parametric approach yield similar results; (2) the incorporation of parameter uncertainty results in much wider probability forecast; and (3) evaluation of density forecasts indicates better performance when only future uncertainties are considered and parameter uncertainties are excluded.

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Bibliographic Info

Article provided by Institute of Public Finance in its journal Financial Theory and Practice.

Volume (Year): 35 (2011)
Issue (Month): 2 ()
Pages: 140-170

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Handle: RePEc:ipf:finteo:v:35:y:2011:i:2:p:140-170

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Related research

Keywords: economic forecasting; density forecasting; fan chart; stochastic simulations; uncertainty; Croatia;

References

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  1. F. Thomas Juster, 1966. "Consumer Buying Intentions and Purchase Probability: An Experiment in Survey Design," NBER Books, National Bureau of Economic Research, Inc, number just66-2, May.
  2. Anthony Tay & Kenneth F. Wallis, 2000. "Density Forecasting: A Survey," Econometric Society World Congress 2000 Contributed Papers 0370, Econometric Society.
  3. Clements, Michael P & Hendry, David F, 1995. "Macro-economic Forecasting and Modelling," Economic Journal, Royal Economic Society, vol. 105(431), pages 1001-13, July.
  4. Garrat, A. & Lee, K. & Pesaran, M.H. & Shin, Y., 2000. "Forecast Uncertainties in Macroeconometric Modelling: An Application to the UK Economy," Cambridge Working Papers in Economics 0004, Faculty of Economics, University of Cambridge.
  5. Diebold, Francis X & Gunther, Todd A & Tay, Anthony S, 1998. "Evaluating Density Forecasts with Applications to Financial Risk Management," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 863-83, November.
  6. Engelberg, Joseph & Manski, Charles F. & Williams, Jared, 2009. "Comparing the Point Predictions and Subjective Probability Distributions of Professional Forecasters," Journal of Business & Economic Statistics, American Statistical Association, vol. 27, pages 30-41.
  7. Ray C. Fair, 1978. "Estimating the Expected Predictive Accuracy of Econometric Models," Cowles Foundation Discussion Papers 480, Cowles Foundation for Research in Economics, Yale University.
  8. Garratt, Anthony & Lee, Kevin & Pesaran, M. Hashem & Shin, Yongcheol, 2012. "Global and National Macroeconometric Modelling: A Long-Run Structural Approach," OUP Catalogue, Oxford University Press, number 9780199650460, October.
  9. Thomas Doan & Robert B. Litterman & Christopher A. Sims, 1983. "Forecasting and Conditional Projection Using Realistic Prior Distributions," NBER Working Papers 1202, National Bureau of Economic Research, Inc.
  10. Victor Zarnowitz & Louis A. Lambros, 1983. "Consensus and Uncertainty in Economic Prediction," NBER Working Papers 1171, National Bureau of Economic Research, Inc.
  11. Zarnowitz, Victor & Lambros, Louis A, 1987. "Consensus and Uncertainty in Economic Prediction," Journal of Political Economy, University of Chicago Press, vol. 95(3), pages 591-621, June.
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