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Does forecast combination improve Norges Bank inflation forecasts?

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Author Info

  • Hilde C. Bjørnland

    (Norwegian School of Management (BI) and Norges Bank (Central Bank of Norway))

  • Karsten Gerdrup

    ()
    (Norges Bank (Central Bank of Norway))

  • Anne Sofie Jore

    ()
    (Norges Bank (Central Bank of Norway))

  • Christie Smith

    ()
    (Norges Bank (Central Bank of Norway))

  • Leif Anders Thorsrud

    ()
    (Norges Bank (Central Bank of Norway))

Abstract

We develop a system that provides model-based forecasts for inflation in Norway. Forecasts are recursively evaluated from 1999 to 2008. The performance of the models over this period is then used to derive weights that are used to combine the forecasts. Our results indicate that model combination improves upon the point forecasts from individual models. Furthermore, when comparing the whole forecasting period; model combination outperforms Norges Banks own point forecast for inflation at the forecast horizon up to a year. By using a suite of models we allow for a greater range of modelling techniques and data to be used in the forecasting process.

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File URL: http://www.norges-bank.no/en/Published/Papers/Working-Papers/2009/WP-20091/
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Bibliographic Info

Paper provided by Norges Bank in its series Working Paper with number 2009/01.

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Length: 35 pages
Date of creation: 27 Jan 2009
Date of revision:
Handle: RePEc:bno:worpap:2009_01

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Keywords: Forecasting; forecast combination;

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References

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  1. Kadiyala, K. Rao & Karlsson, Sune, 1994. "Numerical Aspects of Bayesian VAR-modeling," Working Paper Series in Economics and Finance 12, Stockholm School of Economics.
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  3. Aiolfi, Marco & Timmermann, Allan, 2006. "Persistence in forecasting performance and conditional combination strategies," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 31-53.
  4. Anne-Sofie Jore & James Mitchell & Shaun P. Vahey, 2008. "Combining forecast densities from VARs with uncertain instabilities," Working Paper 2008/01, Norges Bank.
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  7. George Kapetanios & Vincent Labhard & Simon Price, 2007. "Forecast combination and the Bank of England’s suite of statistical forecasting models," Bank of England working papers 323, Bank of England.
  8. Jeremy Smith & Kenneth F. Wallis, 2009. "A Simple Explanation of the Forecast Combination Puzzle," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(3), pages 331-355, 06.
  9. Todd E. Clark & Michael W. McCracken, 2007. "Averaging forecasts from VARs with uncertain instabilities," Finance and Economics Discussion Series 2007-42, Board of Governors of the Federal Reserve System (U.S.).
  10. Makridakis, Spyros & Hibon, Michele, 2000. "The M3-Competition: results, conclusions and implications," International Journal of Forecasting, Elsevier, vol. 16(4), pages 451-476.
  11. Ashley, Richard, 2003. "Statistically significant forecasting improvements: how much out-of-sample data is likely necessary?," International Journal of Forecasting, Elsevier, vol. 19(2), pages 229-239.
  12. Spyros Makridakis & Robert L. Winkler, 1983. "Averages of Forecasts: Some Empirical Results," Management Science, INFORMS, vol. 29(9), pages 987-996, September.
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  14. Knut Are Aastveit & Tørres G. Trovik, 2008. "Nowcasting Norwegian GDP: The role of asset prices in a small open economy," Working Paper 2007/09, Norges Bank.
  15. Don Coletti & Stephen Murchison, 2002. "Models in Policy-Making," Bank of Canada Review, Bank of Canada, vol. 2002(Spring), pages 19-26.
  16. Andersson, Michael K & Karlsson, Sune, 2007. "Bayesian Forecast Combination for VAR Models," Working Papers 2007:13, Örebro University, School of Business.
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Citations

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Cited by:
  1. Knut Are Aastveit & Karsten R. Gerdrup & Anne Sofie Jore & Leif Anders Thorsrud, 2011. "Nowcasting GDP in real-time: A density combination approach," Working Paper 2011/11, Norges Bank.
  2. Sarah Drought & Chris McDonald, 2011. "Forecasting house price inflation: a model combination approach," Reserve Bank of New Zealand Discussion Paper Series DP2011/07, Reserve Bank of New Zealand.
  3. Monica Billio & Roberto Casarin & Francesco Ravazzolo & Herman K. van Dijk, 2012. "Combination schemes for turning point predictions," Working Papers 2012_15, Department of Economics, University of Venice "Ca' Foscari".
  4. Jon D. Samuels & Rodrigo Sekkel, 2013. "Forecasting with Many Models: Model Confidence Sets and Forecast Combination," Working Papers 13-11, Bank of Canada.
  5. Hayashi, Masayoshi, 2014. "Forecasting welfare caseloads: The case of the Japanese public assistance program," Socio-Economic Planning Sciences, Elsevier, vol. 48(2), pages 105-114.
  6. Marek Rusnak, 2013. "Nowcasting Czech GDP in Real Time," Working Papers 2013/06, Czech National Bank, Research Department.
  7. Magnus, J.R. & Wang, W. & Zhang, Xinyu, 2012. "WALS Prediction," Discussion Paper 2012-043, Tilburg University, Center for Economic Research.
  8. Todd E. Clark & Michael W. McCracken, 2013. "Evaluating the accuracy of forecasts from vector autoregressions," Working Papers 2013-010, Federal Reserve Bank of St. Louis.
  9. Kjetil Martinsen & Francesco Ravazzolo & Fredrik Wulfsberg, 2011. "Forecasting macroeconomic variables using disaggregate survey data," Working Paper 2011/04, Norges Bank.
  10. Chris Bloor, 2009. "The use of statistical forecasting models at the Reserve Bank of New Zealand," Reserve Bank of New Zealand Bulletin, Reserve Bank of New Zealand, vol. 72, pages 21-26, June.

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