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A Hybrid Forecasting Approach

Author

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  • Emilian Dobrescu

    (Center of Macroeconomic Modelling, Romanian Academy)

Abstract

The objective of the paper is to establish the appropriateness of integrating in predictive simulation an econometric estimation of a given variable into a standard moving average process (a linear algorithm with constant positive weights of distributed lags). The empirical search relates to the Romanian input-output tables collapsed into ten sectors. The database concerning the final output during 1989-2009 years is herein analyzed

Suggested Citation

  • Emilian Dobrescu, 2014. "A Hybrid Forecasting Approach," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 16(35), pages 390-390, February.
    • Handle: RePEc:aes:amfeco:v:1:y:2014:i:35:p:390
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    References listed on IDEAS

    as
    1. Dobrescu, Emilian, 2013. "Modelling the Sectoral Structure of the Final Output," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 59-89, October.
    2. Mariola Pilatowska, 2009. "The Combined Forecasts Using the Akaike Weights," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 9, pages 5-16.
    3. Gary Koop & Dimitris Korobilis, 2012. "Forecasting Inflation Using Dynamic Model Averaging," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 53(3), pages 867-886, August.
    4. Ravazzolo, F. & van Dijk, H.K. & Verbeek, M.J.C.M., 2007. "Predictive gains from forecast combinations using time-varying model weights," Econometric Institute Research Papers EI 2007-26, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    5. Rodrigues, Bruno Dore & Stevenson, Maxwell J., 2013. "Takeover prediction using forecast combinations," International Journal of Forecasting, Elsevier, vol. 29(4), pages 628-641.
    6. Gregory, Allan W. & Yetman, James, 2004. "The evolution of consensus in macroeconomic forecasting," International Journal of Forecasting, Elsevier, vol. 20(3), pages 461-473.
    7. Gregory, Allan W & Smith, Gregor W & Yetman, James, 2001. "Testing for Forecast Consensus," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(1), pages 34-43, January.
    8. David C. Schirm, 2003. "A Comparative Analysis of the Rationality of Consensus Forecasts of U.S. Economic Indicators," The Journal of Business, University of Chicago Press, vol. 76(4), pages 547-562, October.
    9. Miller,Ronald E. & Blair,Peter D., 2009. "Input-Output Analysis," Cambridge Books, Cambridge University Press, number 9780521517133.
    10. Ager, P. & Kappler, M. & Osterloh, S., 2009. "The accuracy and efficiency of the Consensus Forecasts: A further application and extension of the pooled approach," International Journal of Forecasting, Elsevier, vol. 25(1), pages 167-181.
    11. Robert L. Winkler & Robert T. Clemen, 1992. "Sensitivity of Weights in Combining Forecasts," Operations Research, INFORMS, vol. 40(3), pages 609-614, June.
    12. Miller,Ronald E. & Blair,Peter D., 2009. "Input-Output Analysis," Cambridge Books, Cambridge University Press, number 9780521739023.
    13. Emilian Dobrescu & Viorel Gaftea, 2012. "On the Accuracy of RAS Method in an Emergent Economy," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 14(32), pages 502-521, June.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    final output extrapolation; hybrid approach.;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C67 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Input-Output Models

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