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Hvor presise er prognosene i Nasjonalbudsjettet?
[How precise are the forecasts of the Norwegian national budget?]

Author

Listed:
  • Gharsallah, Sofian
  • Sucarrat, Genaro

Abstract

Årlige prognoser av norsk økonomi er av stor viktighet for beslutningstakere. Dette gjelder spesielt stortingspolitikerne som vedtar Statsbudsjettet basert på prognosene i Nasjonalbudsjettet. Disse prognosene utarbeides av Finansdepartementet (FIN). I dette studiet evaluerer vi presisjonen til et utvalg prognoser i perioden 1999 - 2018. Til sammenligning inkluderer vi prognosene til Norges Bank og Statistisk sentralbyrå, og prognosene til tre enkle modeller: Snittet, tilfeldig gange og AR(1) modellen. Vi finner ingen generell støtte for hypotesen om at prognosene til enkle modeller er mer presise enn de til Nasjonalbudsjettet. Videre finner vi at Nasjonalbudsjettets prognoser er: Generelt litt mer presise enn de til enkle modeller, på nivå med prognosene til Norges Bank og SSB, og i hovedsak ubetinget forventningsrette (dvs. at de i gjennomsnitt treffer det de sikter på).

Suggested Citation

  • Gharsallah, Sofian & Sucarrat, Genaro, 2019. "Hvor presise er prognosene i Nasjonalbudsjettet? [How precise are the forecasts of the Norwegian national budget?]," MPRA Paper 96850, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:96850
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    References listed on IDEAS

    as
    1. Jacob A. Mincer & Victor Zarnowitz, 1969. "The Evaluation of Economic Forecasts," NBER Chapters, in: Economic Forecasts and Expectations: Analysis of Forecasting Behavior and Performance, pages 3-46, National Bureau of Economic Research, Inc.
    2. Whitney Newey & Kenneth West, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    3. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
    Full references (including those not matched with items on IDEAS)

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    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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