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Are Inflation Forecasts from Major Swedish Forecasters Biased?

Author

Listed:
  • Lundholm, Michael

    (Dept. of Economics, Stockholm University)

Abstract

Inflation forecasts made 1999-2005 by Sveriges Riksbank and Konjunkturinstitet of Swedish inflation rates 1999-2007 are tested for unbiasedness; i.e., are the mean forecast errors zero? The bias is in the order of -0.1 percentage units for horizons below one year and in the order of 0.1 and 0.6 (depending on inflation measure) above one year. Using the maximum entropy bootstrap for inference bias is significant whereas inference using HAC indicates insignificance.

Suggested Citation

  • Lundholm, Michael, 2010. "Are Inflation Forecasts from Major Swedish Forecasters Biased?," Research Papers in Economics 2010:10, Stockholm University, Department of Economics.
    • Handle: RePEc:hhs:sunrpe:2010_0010
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    File URL: http://www2.ne.su.se/paper/wp10_10.pdf
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    References listed on IDEAS

    as
    1. Jansson, Per & Vredin, Anders, 2003. "Forecast-Based Monetary Policy: The Case of Sweden," International Finance, Wiley Blackwell, vol. 6(3), pages 349-380, Winter.
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    4. Per Jansson & Anders Vredin, 2003. "Forecast‐Based Monetary Policy: The Case of Sweden," International Finance, Wiley Blackwell, vol. 6(3), pages 349-380, November.
    5. Vinod, Hrishikesh D., 2006. "Maximum entropy ensembles for time series inference in economics," Journal of Asian Economics, Elsevier, vol. 17(6), pages 955-978, December.
    6. Newey, Whitney & West, Kenneth, 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.
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    More about this item

    Keywords

    Forecast evaluation; inflation; unbiasedness; maximum entropy bootstrap;
    All these keywords.

    JEL classification:

    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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