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Assessing forecast performance

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Abstract

Assessing the accuracy of macroeconomic forecasts is critical to identifying opportunities to improve macroeconomic advice. For example, revealing and then removing bias – from either persistently under- or over-estimating economic activity – would allow a policymaker to better assess the state of the economy, improving monetary (and fiscal) policy. Policy might also be improved by incorporating the beliefs of forecasters with a good track record.

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  • Kirdan Lees, 2016. "Assessing forecast performance," Reserve Bank of New Zealand Bulletin, Reserve Bank of New Zealand, vol. 79, pages 1-19., June.
    • Handle: RePEc:nzb:nzbbul:jun2016:10
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    References listed on IDEAS

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    1. Andrew Bauer & Robert A. Eisenbeis & Daniel F. Waggoner & Tao Zha, 2003. "Forecast evaluation with cross-sectional data: The Blue Chip Surveys," Economic Review, Federal Reserve Bank of Atlanta, vol. 88(Q2), pages 17-31.
    2. Kilian, Lutz & Taylor, Mark P., 2003. "Why is it so difficult to beat the random walk forecast of exchange rates?," Journal of International Economics, Elsevier, vol. 60(1), pages 85-107, May.
    3. Sarno,Lucio & Taylor,Mark P., 2003. "The Economics of Exchange Rates," Cambridge Books, Cambridge University Press, number 9780521485845.
    4. Felipe Labbe & Hamish Pepper, 2009. "Assessing recent external forecasts," Reserve Bank of New Zealand Bulletin, Reserve Bank of New Zealand, vol. 72, pages 19-25, December.
    5. Mr. Allan Timmermann, 2006. "An Evaluation of the World Economic Outlook Forecasts," IMF Working Papers 2006/059, International Monetary Fund.
    6. Krol, Robert, 2013. "Evaluating state revenue forecasting under a flexible loss function," International Journal of Forecasting, Elsevier, vol. 29(2), pages 282-289.
    7. Christine Lewis & Nigel Pain, 2014. "Lessons from OECD forecasts during and after the financial crisis," OECD Journal: Economic Studies, OECD Publishing, vol. 2014(1), pages 9-39.
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    Cited by:

    1. James Yetman, 2018. "The perils of approximating fixed-horizon inflation forecasts with fixed-event forecasts," BIS Working Papers 700, Bank for International Settlements.
    2. Kapur, Muneesh, 2018. "Macroeconomic Policies and Transmission Dynamics in India," MPRA Paper 88566, University Library of Munich, Germany.
    3. Viv B. Hall & Peter Thomson, 2021. "Does Hamilton’s OLS Regression Provide a “better alternative” to the Hodrick-Prescott Filter? A New Zealand Business Cycle Perspective," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 17(2), pages 151-183, November.
    4. repec:nzb:nzbbul:jul2016:07 is not listed on IDEAS
    5. Viv B Hall & Peter Thomson, 2020. "Does Hamilton’s OLS regression provide a “better alternative†to the Hodrick-Prescott filter? A New Zealand business cycle perspective," CAMA Working Papers 2020-71, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    6. Adam Richardson, 2016. "Behind the scenes of an OCR decision in New Zealand," Reserve Bank of New Zealand Bulletin, Reserve Bank of New Zealand, vol. 79, pages 1-15, July.
    7. André Binette & Dmitri Tchebotarev, 2017. "Evaluating Real GDP Growth Forecasts in the Bank of Canada Monetary Policy Report," Staff Analytical Notes 17-21, Bank of Canada.
    8. Rajesh, Raj & Srivastava, Vineet, 2020. "GDP Growth Forecasts of the Reserve Bank of India – A Performance Assessment," MPRA Paper 104131, University Library of Munich, Germany, revised 11 Oct 2020.

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