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Using forecast evaluation to improve the accuracy of the Greenbook forecast

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  • Arai, Natsuki

Abstract

Recently, Patton and Timmermann (2012) proposed a more powerful kind of forecast efficiency regression at multiple horizons, and showed that it provides evidence against the efficiency of the Fed’s Greenbook forecasts. I use their forecast efficiency evaluation to propose a method for adjusting the Greenbook forecasts. Using this method in a real-time out-of-sample forecasting exercise, I find that it provides modest improvements in the accuracies of the forecasts for the GDP deflator and CPI, but not for other variables. The improvements are statistically significant in some cases, with magnitudes of up to 18% in root mean square prediction error.

Suggested Citation

  • Arai, Natsuki, 2014. "Using forecast evaluation to improve the accuracy of the Greenbook forecast," International Journal of Forecasting, Elsevier, vol. 30(1), pages 12-19.
  • Handle: RePEc:eee:intfor:v:30:y:2014:i:1:p:12-19 DOI: 10.1016/j.ijforecast.2013.02.002
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    References listed on IDEAS

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    1. repec:trp:01jefa:jefa0003 is not listed on IDEAS
    2. Levent Bulut, 2017. "Does Statistical Significance Help to Evaluate Predictive Performance of Competing Models?," Journal of Economics and Financial Analysis, Tripal Publishing House, vol. 1(1), pages 1-13.
    3. Soojin Jo & Rodrigo Sekkel, 2016. "Macroeconomic Uncertainty Through the Lens of Professional Forecasters," Staff Working Papers 16-5, Bank of Canada.
    4. Chang, Andrew C. & Hanson, Tyler J., 2015. "The Accuracy of Forecasts Prepared for the Federal Open Market Committee," Finance and Economics Discussion Series 2015-62, Board of Governors of the Federal Reserve System (U.S.).
    5. Chang, Andrew C. & Hanson, Tyler J., 2016. "The accuracy of forecasts prepared for the Federal Open Market Committee," Journal of Economics and Business, Elsevier, pages 23-43.

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