IDEAS home Printed from https://ideas.repec.org/a/bxr/bxrceb/y2004v47i2p249-278.html
   My bibliography  Save this article

How accurate are the Swedish forecasters on GDB-Growth, CPI-inflation and unemployment? (1993 - 2001)

Author

Listed:
  • Bharat Barot

Abstract

This study evaluates the performance of the eight most important Swedish domestic forecasters of real GDP-growth, CPI-inflation and unemployment for the sample period 1993-2001. The evaluation is based on the following measures: mean absolute error, the root mean square error, bias and finally directional accuracy. The forecasts are even compared to naive random walk and random walk with drift models. The results indicate that the current forecasts compared to the year ahead forecasts decline over the forecasting horizons as more information becomes available. The results with respect to the directional accuracy indicate that we are equally good/bad in predicting the directional accuracy for all three macro aggregates. According to the comparisons with the naive random walk model six out of seven Swedish CPI-inflation forecasters were outperformed by the naive random walk model. Tests of bias indicate that the Swedish forecasters underestimate GDP-growth and overestimate CPI-inflation and the unemployment rate for the sample period. All the Swedish forecasters have been successful in predicting the downward trend in CPI-inflation and the unemployment rate. The performance of the Swedish domestic forecasters is better using preliminary GDP-growth outcomes than final. The performance for the current year forecasts is better than the year ahead forecasts for all three macro economic variables. Revisions are positively biased.

Suggested Citation

  • Bharat Barot, 2004. "How accurate are the Swedish forecasters on GDB-Growth, CPI-inflation and unemployment? (1993 - 2001)," Brussels Economic Review, ULB -- Universite Libre de Bruxelles, vol. 47(2), pages 249-278.
  • Handle: RePEc:bxr:bxrceb:y:2004:v:47:i:2:p:249-278
    as

    Download full text from publisher

    File URL: https://dipot.ulb.ac.be/dspace/bitstream/2013/11937/1/ber-0294.pdf
    File Function: ber-0294
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Francis X. Diebold & Anthony S. Tay & Kenneth F. Wallis, 1997. "Evaluating Density Forecasts of Inflation: The Survey of Professional Forecasters," NBER Working Papers 6228, National Bureau of Economic Research, Inc.
    2. Christina D. Romer & David H. Romer, 1996. "Federal Reserve Private Information and the Behavior of Interest Rates," NBER Working Papers 5692, National Bureau of Economic Research, Inc.
    3. Francis X. Diebold & Todd A. Gunther & Anthony S. Tay, "undated". "Evaluating Density Forecasts," CARESS Working Papres 97-18, University of Pennsylvania Center for Analytic Research and Economics in the Social Sciences.
    4. N/A, 1969. "How Well Does the National Institute Forecast ?," National Institute Economic Review, National Institute of Economic and Social Research, vol. 50(1), pages 40-52, November.
    5. Clements, Michael P. & Hendry, David F., 1997. "An empirical study of seasonal unit roots in forecasting," International Journal of Forecasting, Elsevier, vol. 13(3), pages 341-355, September.
    6. Armstrong, J. Scott & Collopy, Fred, 1992. "Error measures for generalizing about forecasting methods: Empirical comparisons," International Journal of Forecasting, Elsevier, vol. 8(1), pages 69-80, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Periklis Gogas & Theophilos Papadimitriou & Emmanouil Sofianos, 2022. "Forecasting unemployment in the euro area with machine learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 551-566, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. repec:lan:wpaper:470 is not listed on IDEAS
    2. Oller, Lars-Erik & Barot, Bharat, 2000. "The accuracy of European growth and inflation forecasts," International Journal of Forecasting, Elsevier, vol. 16(3), pages 293-315.
    3. Barot, Bharat, 2007. "Empirical Studies in Consumption, House Prices and the Accuracy of European Growth and Inflation Forecasts," Working Papers 98, National Institute of Economic Research.
    4. repec:lan:wpaper:425 is not listed on IDEAS
    5. repec:lan:wpaper:539557 is not listed on IDEAS
    6. repec:lan:wpaper:413 is not listed on IDEAS
    7. von der Gracht, Heiko A. & Hommel, Ulrich & Prokesch, Tobias & Wohlenberg, Holger, 2016. "Testing weighting approaches for forecasting in a Group Wisdom Support System environment," Journal of Business Research, Elsevier, vol. 69(10), pages 4081-4094.
    8. Boero, Gianna & Smith, Jeremy & Wallis, Kenneth F, 2002. "The Properties Of Some Goodness-Of-Fit Tests," The Warwick Economics Research Paper Series (TWERPS) 653, University of Warwick, Department of Economics.
    9. Clements, Michael P., 2006. "Internal consistency of survey respondentsíforecasts: Evidence based on the Survey of Professional Forecasters," Economic Research Papers 269742, University of Warwick - Department of Economics.
    10. Francis X. Diebold & Jinyong Hahn & Anthony S. Tay, 1998. "Real-Time Multivariate Density Forecast Evaluation and Calibration: Monitoring the Risk of High-Frequency Returns on Foreign Exchange," New York University, Leonard N. Stern School Finance Department Working Paper Seires 98-079, New York University, Leonard N. Stern School of Business-.
    11. Corradi, Valentina & Swanson, Norman R., 2006. "Bootstrap conditional distribution tests in the presence of dynamic misspecification," Journal of Econometrics, Elsevier, vol. 133(2), pages 779-806, August.
    12. Sean D. Campbell & Francis X. Diebold, 2005. "Weather Forecasting for Weather Derivatives," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 6-16, March.
    13. Jordi Pons, 2001. "The rationality of price forecasts: a directional analysis," Applied Financial Economics, Taylor & Francis Journals, vol. 11(3), pages 287-290.
    14. Mestre, Ricardo, 2007. "Are survey-based inflation expections in the euro area informative?," Working Paper Series 721, European Central Bank.
    15. Valentina Corradi & Norman R. Swanson, 2003. "A Test for Comparing Multiple Misspecified Conditional Distributions," Departmental Working Papers 200314, Rutgers University, Department of Economics.
    16. Wallis, Kenneth F., 2003. "Chi-squared tests of interval and density forecasts, and the Bank of England's fan charts," International Journal of Forecasting, Elsevier, vol. 19(2), pages 165-175.
    17. Amisano, Gianni & Giacomini, Raffaella, 2007. "Comparing Density Forecasts via Weighted Likelihood Ratio Tests," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 177-190, April.
    18. Alquier Pierre & Li Xiaoyin & Wintenberger Olivier, 2014. "Prediction of time series by statistical learning: general losses and fast rates," Dependence Modeling, De Gruyter, vol. 1(2013), pages 65-93, January.
    19. Arrieta-Prieto, Mario & Schell, Kristen R., 2022. "Spatio-temporal probabilistic forecasting of wind power for multiple farms: A copula-based hybrid model," International Journal of Forecasting, Elsevier, vol. 38(1), pages 300-320.
    20. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    21. Kreye, M.E. & Goh, Y.M. & Newnes, L.B. & Goodwin, P., 2012. "Approaches to displaying information to assist decisions under uncertainty," Omega, Elsevier, vol. 40(6), pages 682-692.
    22. Jan G. De Gooijer & Rob J. Hyndman, 2005. "25 Years of IIF Time Series Forecasting: A Selective Review," Monash Econometrics and Business Statistics Working Papers 12/05, Monash University, Department of Econometrics and Business Statistics.
    23. Gabriela de Raaij & Burkhard Raunig, 2002. "Evaluating Density Forecasts with an Application to Stock Market Returns," Working Papers 59, Oesterreichische Nationalbank (Austrian Central Bank).
    24. Wang, Yudong & Zhang, Bing & Diao, Xundi & Wu, Chongfeng, 2015. "Commodity price changes and the predictability of economic policy uncertainty," Economics Letters, Elsevier, vol. 127(C), pages 39-42.

    More about this item

    Keywords

    Mean absolute error; root mean square error; directional accuracy; bias; revisions; final respective preliminary outcomes; Theil index; naïve forecasts;
    All these keywords.

    JEL classification:

    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bxr:bxrceb:y:2004:v:47:i:2:p:249-278. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Benoit Pauwels (email available below). General contact details of provider: https://edirc.repec.org/data/dulbebe.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.