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The Riksbank’s Forecasting Performance

Author

Listed:
  • Andersson, Michael K.

    (Monetary Policy Department, Central Bank of Sweden)

  • Karlsson, Gustav

    (Monetary Policy Department, Central Bank of Sweden)

  • Svensson, Josef

    (Monetary Policy Department, Central Bank of Sweden)

Abstract

This paper describes the official Riksbank forecasts for the period 2000-06. The forecast variables are those that are important for monetary policy analysis, i.e. inflation, GDP, productivity, employment, labour force, unemployment and financial variables such as interest rate and foreign exchange rate. The Riksbank’s forecasts are presented and analyzed and compared with alternative forecasts, that is, those from other institutions and simple statistical models. One important message from the study is that macroeconomic forecasts are associated with an appreciable uncertainty; the forecast errors are often sizeable. The forecast memory, defined as how far the forecasts are more informative than the variables unconditional mean, is usually limited to the first year. Furthermore, we find that the inflation forecasts exhibit several appealing features, such as a predictability memory that (possibly) includes the second year, relatively low RMSE and weak efficiency. The forecasts for the investigated real variables are shown to be less precise and they have a shorter forecast memory. The exchange rate predictions demonstrate the least accurate (of the investigated variables) forecasts. Compared to other forecasters, the Riksbank’s predictions are often more accurate. This holds for a comparison with the National Institute of Economic Research, even though the differences are statistically insignificant, as well as for a comparison with the participants in the Consensus Forecasts panel, where the Riksbank’s predictions often are among the best. We also find indications that misjudgements for productivity growth have had effects on forecasts for both inflation and GDP, but the results suggest that the Riksbank has considered available information in an acceptable fashion. This is also true for the undertaken revisions (from one forecast occasion to another) of the published forecasts.

Suggested Citation

  • Andersson, Michael K. & Karlsson, Gustav & Svensson, Josef, 2007. "The Riksbank’s Forecasting Performance," Working Paper Series 218, Sveriges Riksbank (Central Bank of Sweden).
  • Handle: RePEc:hhs:rbnkwp:0218
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    References listed on IDEAS

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    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. Charles Engel & Kenneth D. West, 2005. "Exchange Rates and Fundamentals," Journal of Political Economy, University of Chicago Press, vol. 113(3), pages 485-517, June.
    3. Richard Meese & Kenneth Rogoff, 1983. "The Out-of-Sample Failure of Empirical Exchange Rate Models: Sampling Error or Misspecification?," NBER Chapters, in: Exchange Rates and International Macroeconomics, pages 67-112, National Bureau of Economic Research, Inc.
    4. Andersson, Michael K., 1998. "Do Long-Memory Models Have Long Memory?," SSE/EFI Working Paper Series in Economics and Finance 227, Stockholm School of Economics, revised 16 Mar 2000.
    5. Malin Adolfson & Michael K. Andersson & Jesper Lindé & Mattias Villani & Anders Vredin, 2007. "Modern Forecasting Models in Action: Improving Macroeconomic Analyses at Central Banks," International Journal of Central Banking, International Journal of Central Banking, vol. 3(4), pages 111-144, December.
    6. John G. Galbraith & Greg Tkacz, 2006. "How Far Can We Forecast? Forecast Content Horizons For Some Important Macroeconomic Time Series," Departmental Working Papers 2006-13, McGill University, Department of Economics.
    7. Yock Y. Chong & David F. Hendry, 1986. "Econometric Evaluation of Linear Macro-Economic Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 53(4), pages 671-690.
    8. Nordhaus, William D, 1987. "Forecasting Efficiency: Concepts and Applications," The Review of Economics and Statistics, MIT Press, vol. 69(4), pages 667-674, November.
    9. Adolfson, Malin & Laséen, Stefan & Lindé, Jesper & Villani, Mattias, 2008. "Evaluating an estimated new Keynesian small open economy model," Journal of Economic Dynamics and Control, Elsevier, vol. 32(8), pages 2690-2721, August.
    10. Andersson, Michael K., 2000. "Do long-memory models have long memory?," International Journal of Forecasting, Elsevier, vol. 16(1), pages 121-124.
    11. Berg, Claes & Jansson, Per & Vredin, Anders, 2004. "How Useful are Simple Rules for Monetary Policy? The Swedish Experience," Working Paper Series 169, Sveriges Riksbank (Central Bank of Sweden).
    12. Sean D. Campbell, 2004. "Macroeconomic volatility, predictability and uncertainty in the Great Moderation: evidence from the survey of professional forecasters," Finance and Economics Discussion Series 2004-52, Board of Governors of the Federal Reserve System (U.S.).
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    Cited by:

    1. Carlos Medel, 2017. "Forecasting Chilean inflation with the hybrid new keynesian Phillips curve: globalisation, combination, and accuracy," Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 20(3), pages 004-050, December.
    2. Carlos A. Medel, 2018. "Forecasting Inflation with the Hybrid New Keynesian Phillips Curve: A Compact-Scale Global VAR Approach," International Economic Journal, Taylor & Francis Journals, vol. 32(3), pages 331-371, July.
    3. Pablo Pincheira & Carlos A. Medel, 2012. "Forecasting Inflation with a Simple and Accurate Benchmark: a Cross-Country Analysis," Working Papers Central Bank of Chile 677, Central Bank of Chile.
    4. Pablo Pincheira, 2012. "A Joint Test of Superior Predictive Ability for Chilean Inflation Forecasts," Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 15(3), pages 04-39, December.
    5. Pablo Pincheira, 2010. "A Real Time Evaluation of the Central Bank of Chile GDP Growth Forecasts," Money Affairs, CEMLA, vol. 0(1), pages 37-73, January-J.

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    More about this item

    Keywords

    Judgements; Forecast Evaluation; Central Bank; Inflation; GDP; RMSE;
    All these keywords.

    JEL classification:

    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

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