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Using forecast evaluation to improve the accuracy of the Greenbook forecast

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  • Arai, Natsuki
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    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.

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    Bibliographic Info

    Article provided by Elsevier in its journal International Journal of Forecasting.

    Volume (Year): 30 (2014)
    Issue (Month): 1 ()
    Pages: 12-19

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    Handle: RePEc:eee:intfor:v:30:y:2014:i:1:p:12-19

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    Web page: http://www.elsevier.com/locate/ijforecast

    Related research

    Keywords: Evaluating forecasts; Forecast efficiency; Adjusting forecasts; Real-time data; The Greenbook forecast;

    References

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    1. West, Kenneth D., 2006. "Forecast Evaluation," Handbook of Economic Forecasting, Elsevier.
    2. Edge, Rochelle M & Gürkaynak, Refet S., 2010. "How Useful Are Estimated DSGE Model Forecasts for Central Bankers?," CEPR Discussion Papers 8158, C.E.P.R. Discussion Papers.
    3. Todd E. Clark & Michael W. McCracken, 2011. "Advances in forecast evaluation," Working Papers 2011-025, Federal Reserve Bank of St. Louis.
    4. Peter Tulip, 2009. "Has the Economy Become More Predictable? Changes in Greenbook Forecast Accuracy," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 41(6), pages 1217-1231, 09.
    5. Francis X. Diebold & Robert S. Mariano, 1994. "Comparing Predictive Accuracy," NBER Technical Working Papers 0169, National Bureau of Economic Research, Inc.
    6. Christopher A. Sims, 2002. "The Role of Models and Probabilities in the Monetary Policy Process," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 33(2), pages 1-62.
    7. David H. Romer & Christina D. Romer, 2000. "Federal Reserve Information and the Behavior of Interest Rates," American Economic Review, American Economic Association, vol. 90(3), pages 429-457, June.
    8. Faust, Jon & Wright, Jonathan H., 2009. "Comparing Greenbook and Reduced Form Forecasts Using a Large Realtime Dataset," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 468-479.
    9. Fred Joutz & Michael P. Clements & Herman O. Stekler, 2007. "An evaluation of the forecasts of the federal reserve: a pooled approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(1), pages 121-136.
    10. Croushore, Dean, 2006. "Forecasting with Real-Time Macroeconomic Data," Handbook of Economic Forecasting, Elsevier.
    11. repec:taf:jnlbes:v:30:y:2012:i:1:p:1-17 is not listed on IDEAS
    12. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    13. David 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.).
    14. Todd E. Clark & Michael W. McCracken, 2009. "Nested forecast model comparisons: a new approach to testing equal accuracy," Research Working Paper RWP 09-11, Federal Reserve Bank of Kansas City.
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