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The Empirical (Ir)Relevance of the Interest Rate Assumption for Central Bank Forecasts

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  • Knüppel, Malte
  • Schultefrankenfeld, Guido

Abstract

The interest rate assumptions for macroeconomic forecasts differ considerably among central banks. Common approaches are given by the assumption of constant interest rates, interest rates expected by market participants, or the central bank's own interest rate expectations. From a theoretical point of view, the latter should yield the highest forecast accuracy. The lowest accuracy can be expected from forecasts conditioned on constant interest rates. However, when investigating the predictive accuracy of the forecasts for interest rates, inflation and output growth made by the Bank of England and the Banco do Brasil, we hardly find any significant differences between the forecasts based on different interest assumptions. We conclude that the choice of the interest rate assumption, while being a major concern from a theoretical point of view, appears to be at best of minor relevance empirically.

Suggested Citation

  • Knüppel, Malte & Schultefrankenfeld, Guido, 2013. "The Empirical (Ir)Relevance of the Interest Rate Assumption for Central Bank Forecasts," VfS Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 80042, Verein für Socialpolitik / German Economic Association.
  • Handle: RePEc:zbw:vfsc13:80042
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    1. Kajal Lahiri & Huaming Peng & Xuguang Simon Sheng, 2022. "Measuring Uncertainty of a Combined Forecast and Some Tests for Forecaster Heterogeneity," Advances in Econometrics, in: Essays in Honor of M. Hashem Pesaran: Prediction and Macro Modeling, volume 43, pages 29-50, Emerald Group Publishing Limited.
    2. Leeper, Eric M. & Zha, Tao, 2003. "Modest policy interventions," Journal of Monetary Economics, Elsevier, vol. 50(8), pages 1673-1700, November.
    3. Stefan Laséen & Lars E.O. Svensson, 2011. "Anticipated Alternative policy Rate Paths in Plicy Simulations," International Journal of Central Banking, International Journal of Central Banking, vol. 7(3), pages 1-35, September.
    4. Antonello D'Agostino & Domenico Giannone & Paolo Surico, 2005. "(Un)Predictability and Macroeconomic Stability," Macroeconomics 0510024, University Library of Munich, Germany.
    5. Galí, Jordi, 2011. "Are central banks' projections meaningful?," Journal of Monetary Economics, Elsevier, vol. 58(6), pages 537-550.
    6. Charles Goodhart, 2009. "The Interest Rate Conditioning Assumption," International Journal of Central Banking, International Journal of Central Banking, vol. 5(2), pages 85-108, June.
    7. Michael Woodford, 2005. "Central bank communication and policy effectiveness," Proceedings - Economic Policy Symposium - Jackson Hole, Federal Reserve Bank of Kansas City, issue Aug, pages 399-474.
    8. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016. "Measuring Economic Policy Uncertainty," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1593-1636.
    9. Groen, Jan J.J. & Kapetanios, George & Price, Simon, 2009. "A real time evaluation of Bank of England forecasts of inflation and growth," International Journal of Forecasting, Elsevier, vol. 25(1), pages 74-80.
    10. Olivier Blanchard & Jordi Galí, 2007. "Real Wage Rigidities and the New Keynesian Model," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(s1), pages 35-65, February.
    11. Malte Knüppel & Guido Schultefrankenfeld, 2017. "Interest rate assumptions and predictive accuracy of central bank forecasts," Empirical Economics, Springer, vol. 53(1), pages 195-215, August.
    12. Lars E.O. Svensson, 2006. "The Instrument-Rate Projection under Inflation Targeting: The Norwegian Example," Working Papers 75, Princeton University, Department of Economics, Center for Economic Policy Studies..
    13. Lutkepohl, Helmut, 1981. "A model for non-negative and non-positive distributed lag functions," Journal of Econometrics, Elsevier, vol. 16(2), pages 211-219, June.
    14. Malin Adolfson & Stefan Laséen & Jesper Lindé & Mattias Villani, 2005. "Are Constant Interest Rate Forecasts Modest Policy Interventions? Evidence from a Dynamic Open‐Economy Model," International Finance, Wiley Blackwell, vol. 8(3), pages 509-544, December.
    15. 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.
    16. James H. Stock & Mark W. Watson, 2001. "Vector Autoregressions," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 101-115, Fall.
    17. Lars E.O. Svensson, 2006. "The Instrument-Rate Projection under Inflation Targeting: The Norwegian Example," Working Papers 75, Princeton University, Department of Economics, Center for Economic Policy Studies..
    18. repec:pri:cepsud:127svensson is not listed on IDEAS
    19. David L. Reifschneider & Peter Tulip, 2007. "Gauging the uncertainty of the economic outlook from historical forecasting errors," Finance and Economics Discussion Series 2007-60, Board of Governors of the Federal Reserve System (U.S.).
    20. Gneiting, Tilmann, 2011. "Making and Evaluating Point Forecasts," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 746-762.
    21. Raffaella Giacomini & Halbert White, 2006. "Tests of Conditional Predictive Ability," Econometrica, Econometric Society, vol. 74(6), pages 1545-1578, November.
    22. Nicola Anderson & John Sleath, 2001. "New estimates of the UK real and nominal yield curves," Bank of England working papers 126, Bank of England.
    23. Andrews, Donald W K, 1991. "Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation," Econometrica, Econometric Society, vol. 59(3), pages 817-858, May.
    24. Kenneth F. Wallis, 2004. "An Assessment of Bank of England and National Institute Inflation Forecast Uncertainties," National Institute Economic Review, National Institute of Economic and Social Research, vol. 189(1), pages 64-71, July.
    25. Tim Hampton, 2002. "The role of the Reserve Bank's macro model in the formation of interest rate projections," Reserve Bank of New Zealand Bulletin, Reserve Bank of New Zealand, vol. 65, June.
    26. Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
    27. Christoffel, Kai & Coenen, Gunter & Warne, Anders, 2007. "Conditional versus unconditional forecasting with the New Area-Wide Model of the euro area," MPRA Paper 76759, University Library of Munich, Germany.
    28. Faust, Jon & Wright, Jonathan H., 2008. "Efficient forecast tests for conditional policy forecasts," Journal of Econometrics, Elsevier, vol. 146(2), pages 293-303, October.
    29. 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.
    30. Stephen Morris & Hyun Song Shin, 2002. "Social Value of Public Information," American Economic Review, American Economic Association, vol. 92(5), pages 1521-1534, December.
    31. G. Elliott & C. Granger & A. Timmermann (ed.), 2013. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 2, number 2.
    32. Refet S. Gürkaynak, 2005. "Using federal funds futures contracts for monetary policy analysis," Finance and Economics Discussion Series 2005-29, Board of Governors of the Federal Reserve System (U.S.).
    33. James Mitchell & Kenneth F. Wallis, 2011. "Evaluating density forecasts: forecast combinations, model mixtures, calibration and sharpness," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(6), pages 1023-1040, September.
    34. Rochelle M. Edge & Refet S. Gurkaynak, 2010. "How Useful Are Estimated DSGE Model Forecasts for Central Bankers?," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 41(2 (Fall)), pages 209-259.
    35. Andersson, Magnus & Hofmann, Boris, 2009. "Gauging the effectiveness of quantitative forward guidance: evidence from three inflation targeters," Working Paper Series 1098, European Central Bank.
    36. Winkelmann, Lars, 2010. "The Norges Bank's key rate projections and the news element of monetary policy: A wavelet based jump detection approach," SFB 649 Discussion Papers 2010-062, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
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    Cited by:

    1. Carola Conces Binder & Rodrigo Sekkel, 2024. "Central bank forecasting: A survey," Journal of Economic Surveys, Wiley Blackwell, vol. 38(2), pages 342-364, April.
    2. Knüppel, Malte & Schultefrankenfeld, Guido, 2019. "Assessing the uncertainty in central banks’ inflation outlooks," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1748-1769.
    3. Monica Jain & Christopher S. Sutherland, 2020. "How Do Central Bank Projections and Forward Guidance Influence Private-Sector Forecasts?," International Journal of Central Banking, International Journal of Central Banking, vol. 16(5), pages 179-218, October.
    4. Guido Schultefrankenfeld, 2020. "Appropriate monetary policy and forecast disagreement at the FOMC," Empirical Economics, Springer, vol. 58(1), pages 223-255, January.
    5. Reifschneider, David & Tulip, Peter, 2019. "Gauging the uncertainty of the economic outlook using historical forecasting errors: The Federal Reserve’s approach," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1564-1582.
    6. Glas, Alexander & Heinisch, Katja, 2021. "Conditional macroeconomic forecasts: Disagreement, revisions and forecast errors," IWH Discussion Papers 7/2021, Halle Institute for Economic Research (IWH).
    7. Glas, Alexander & Heinisch, Katja, 2023. "Conditional macroeconomic survey forecasts: Revisions and errors," Journal of International Money and Finance, Elsevier, vol. 138(C).
    8. Marc-Oliver Pohle, 2020. "The Murphy Decomposition and the Calibration-Resolution Principle: A New Perspective on Forecast Evaluation," Papers 2005.01835, arXiv.org.
    9. Malte Knüppel & Guido Schultefrankenfeld, 2017. "Interest rate assumptions and predictive accuracy of central bank forecasts," Empirical Economics, Springer, vol. 53(1), pages 195-215, August.
    10. Otmar Issing, 2013. "A New Paradigm for Monetary Policy?," International Finance, Wiley Blackwell, vol. 16(2), pages 273-288, June.
    11. Issing, Otmar, 2013. "A new paradigm for monetary policy?," CFS Working Paper Series 2013/02, Center for Financial Studies (CFS).
    12. Robert P. Lieli & Augusto Nieto-Barthaburu, 2023. "Forecasting with Feedback," Papers 2308.15062, arXiv.org, revised Aug 2024.

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    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies

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