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Forecasting professional forecasters

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Author Info

  • Eric Ghysels
  • Jonathan H. Wright

Abstract

Survey of forecasters, containing respondents' predictions of future values of growth, inflation and other key macroeconomic variables, receive a lot of attention in the financial press, from investors, and from policy makers. They are apparently widely perceived to provide useful information about agents' expectations. Nonetheless, these survey forecasts suffer from the crucial disadvantage that they are often quite stale, as they are released only infrequently, such as on a quarterly basis. In this paper, we propose methods for using asset price data to construct daily forecasts of upcoming survey releases, which we can then evaluate. Our methods allow us to estimate what professional forecasters would predict if they were asked to make a forecast each day, making it possible to measure the effects of events and news announcements on expectations. We apply these methods to forecasts for several macroeconomic variables from both the Survey of Professional Forecasters and Consensus Forecasts.

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Bibliographic Info

Paper provided by Board of Governors of the Federal Reserve System (U.S.) in its series Finance and Economics Discussion Series with number 2006-10.

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Date of creation: 2006
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Handle: RePEc:fip:fedgfe:2006-10

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Related research

Keywords: Economic forecasting;

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References

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  1. Eric Ghysels & Pedro Santa-Clara & Rossen Valkanov, 2004. "There is a Risk-Return Tradeoff After All," NBER Working Papers 10913, National Bureau of Economic Research, Inc.
  2. Andrew Ang & Geert Bekaert & Min Wei, 2005. "Do Macro Variables, Asset Markets or Surveys Forecast Inflation Better?," NBER Working Papers 11538, National Bureau of Economic Research, Inc.
  3. 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.
  4. Domenico Giannone & Lucrezia Reichlin & David H Small, 2007. "Nowcasting GDP and Inflation: The Real-Time Informational Content of Macroeconomic Data Releases," Money Macro and Finance (MMF) Research Group Conference 2006 164, Money Macro and Finance Research Group.
  5. Eric Ghysels & Pedro Santa-Clara & Rossen Valkanov, 2004. "Predicting Volatility: Getting the Most out of Return Data Sampled at Different Frequencies," NBER Working Papers 10914, National Bureau of Economic Research, Inc.
  6. Christoph Schleicher, 2003. "Kolmogorov-Wiener Filters for Finite Time Series," Computing in Economics and Finance 2003 109, Society for Computational Economics.
  7. Martin D.D. Evans, 2005. "Where Are We Now? Real-Time Estimates of the Macro Economy," NBER Working Papers 11064, National Bureau of Economic Research, Inc.
  8. Stock, J.H. & Watson, M.W., 1989. "New Indexes Of Coincident And Leading Economic Indicators," Papers 178d, Harvard - J.F. Kennedy School of Government.
  9. Dean Croushore, 1993. "Introducing: the survey of professional forecasters," Business Review, Federal Reserve Bank of Philadelphia, issue Nov, pages 3-15.
  10. Lamont, Owen A., 2002. "Macroeconomic forecasts and microeconomic forecasters," Journal of Economic Behavior & Organization, Elsevier, vol. 48(3), pages 265-280, July.
  11. Ehrbeck, Tilman & Waldmann, Robert, 1996. "Why Are Professional Forecasters Biased? Agency versus Behavioral Explanations," The Quarterly Journal of Economics, MIT Press, vol. 111(1), pages 21-40, February.
  12. Froot, Kenneth A, 1989. " New Hope for the Expectations Hypothesis of the Term Structure of Interest Rates," Journal of Finance, American Finance Association, vol. 44(2), pages 283-305, June.
  13. Yash P. Mehra, 2002. "Survey measures of expected inflation : revisiting the issues of predictive content and rationality," Economic Quarterly, Federal Reserve Bank of Richmond, issue Sum, pages 17-36.
  14. Michael P. Keane & David E. Runkle, 1998. "Are Financial Analysts' Forecasts of Corporate Profits Rational?," Journal of Political Economy, University of Chicago Press, vol. 106(4), pages 768-805, August.
  15. Eric Ghysels & Arthur Sinko & Rossen Valkanov, 2007. "MIDAS Regressions: Further Results and New Directions," Econometric Reviews, Taylor & Francis Journals, vol. 26(1), pages 53-90.
  16. David M. Cutler & James M. Poterba & Lawrence H. Summers, 1988. "What Moves Stock Prices?," NBER Working Papers 2538, National Bureau of Economic Research, Inc.
  17. Eric Ghysels & Pedro Santa-Clara & Rossen Valkanov, 2004. "The MIDAS Touch: Mixed Data Sampling Regression Models," CIRANO Working Papers 2004s-20, CIRANO.
  18. Andrew Atkeson & Lee E. Ohanian., 2001. "Are Phillips curves useful for forecasting inflation?," Quarterly Review, Federal Reserve Bank of Minneapolis, issue Win, pages 2-11.
  19. Graham Elliott & Allan Timmermann & Ivana Komunjer, 2005. "Estimation and Testing of Forecast Rationality under Flexible Loss," Review of Economic Studies, Oxford University Press, vol. 72(4), pages 1107-1125.
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Citations

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Cited by:
  1. Michael P. Clements & Ana Beatriz Galv�o, 2007. "Macroeconomic Forecasting with Mixed Frequency Data: Forecasting US Output Growth," Working Papers 616, Queen Mary, University of London, School of Economics and Finance.
  2. Clements, Michael P & Galvão, Ana Beatriz, 2006. "Macroeconomic Forecasting with Mixed Frequency Data : Forecasting US output growth and inflation," The Warwick Economics Research Paper Series (TWERPS) 773, University of Warwick, Department of Economics.
  3. Charles Engel & John H. Rogers, 2008. "Expected consumption growth from cross-country surveys: implications for assessing international capital markets," International Finance Discussion Papers 949, Board of Governors of the Federal Reserve System (U.S.).
  4. Rodriguez, Abel & Puggioni, Gavino, 2010. "Mixed frequency models: Bayesian approaches to estimation and prediction," International Journal of Forecasting, Elsevier, vol. 26(2), pages 293-311, April.

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