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General correcting formulae for forecasts

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  • Harin, Alexander

Abstract

The concept of unforeseen events is considered as a part of a hypothesis of uncertain future. The applications of the consequences of the hypothesis in utility and prospect theories are reviewed. Partially unforeseen events and their role in forecasting are analyzed. Preliminary preparations are shown to be able, under specified conditions, to quicken the revisions of forecasts and to hedge or diversify financial risks after partially unforeseen events have occurred. General correcting formulae for forecasts are proposed.

Suggested Citation

  • Harin, Alexander, 2014. "General correcting formulae for forecasts," MPRA Paper 55283, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:55283
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    References listed on IDEAS

    as
    1. Carmela Di Mauro & Anna Maffioletti, 2004. "Attitudes to risk and attitudes to uncertainty: experimental evidence," Applied Economics, Taylor & Francis Journals, vol. 36(4), pages 357-372.
    2. Alexander Harin, 2012. "Data Dispersion in Economics (I) - Possibility of Restrictions," Review of Economics & Finance, Better Advances Press, Canada, vol. 2, pages 59-70, August.
    3. Alexander Harin, 2013. "Data dispersion near the boundaries: can it partially explain the problems of decision and utility theories?," Working Papers hal-00851022, HAL.
    4. Castle, Jennifer L. & Doornik, Jurgen A. & Hendry, David F., 2012. "Model selection when there are multiple breaks," Journal of Econometrics, Elsevier, vol. 169(2), pages 239-246.
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    6. Massimiliano Caporin & Michael McAleer, 2010. "A Scientific Classification Of Volatility Models," Journal of Economic Surveys, Wiley Blackwell, vol. 24(1), pages 192-195, February.
    7. Massimiliano Caporin & Michael McAleer, 2011. "Thresholds, news impact surfaces and dynamic asymmetric multivariate GARCH," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 65(2), pages 125-163, May.
    8. Chang, Chia-Lin & Franses, Philip Hans & McAleer, Michael, 2011. "How accurate are government forecasts of economic fundamentals? The case of Taiwan," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1066-1075, October.
    9. Alexander Harin, 2004. "Arrangement infringement possibility approach: some economic features of large-scale events," Economics Bulletin, AccessEcon, vol. 28(11), pages 1.
    10. Alexander Harin, 2012. "Data Dispersion in Economics(II) - Inevitability and Consequences of Restrictions," Review of Economics & Finance, Better Advances Press, Canada, vol. 2, pages 24-36, November.
    11. Alexander Harin, 2006. "Principle of Uncertain Future," Microeconomics harin_alexander.34115-061, Socionet.
    12. Steve Morlidge, 2013. "How Good Is a "Good" Forecast?: Forecast Errors and Their Avoidability," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 30, pages 5-11, Summer.
    13. Harin, Alexander, 2009. "Ruptures in the probability scale? Calculation of ruptures’ dimensions," MPRA Paper 19348, University Library of Munich, Germany.
    14. Tversky, Amos & Wakker, Peter, 1995. "Risk Attitudes and Decision Weights," Econometrica, Econometric Society, vol. 63(6), pages 1255-1280, November.
    15. Harin, Alexander, 2008. "Solution of the Ellsberg paradox by means of the principle of uncertain future," MPRA Paper 8168, University Library of Munich, Germany.
    16. Harin, Alexander, 2009. "General correcting formula of forecasting?," MPRA Paper 15746, University Library of Munich, Germany.
    17. Harin, Alexander, 2007. "Principle of uncertain future and utility," MPRA Paper 1959, University Library of Munich, Germany.
    18. McAleer, Michael & Medeiros, Marcelo C. & Slottje, Daniel, 2008. "A neural network demand system with heteroskedastic errors," Journal of Econometrics, Elsevier, vol. 147(2), pages 359-371, December.
    19. Alexander Harin, 2005. "A Rational Irrational Man," Public Economics 0511005, University Library of Munich, Germany.
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    More about this item

    Keywords

    forecast; uncertainty; risk; utility; decisions; Ellsberg paradox;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

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