Forecast evaluation with cross-sectional data: The Blue Chip Surveys
AbstractIf economic forecasts are to be used for decision making, then being able to evaluate their accuracy is essential. Assessing accuracy using single variables from a forecast is acceptable as a first pass, but this approach has inherent problems. This article addresses some of these problems by evaluating and comparing the general accuracy of a set of multivariate forecasts over time. ; Using the methodology developed in Eisenbeis, Waggoner, and Zha (2002), the authors compare the economic forecasts in the Blue Chip Economic Indicators Survey. The survey, published monthly since 1977, contains forecasts of many macroeconomic variables over a relatively long time span. The forecasters are a mix of economists from major investment banks, corporations, consulting firms, and academic institutions, many of whom have participated in the survey for several years. The survey thus provides a useful set of forecasts to explore the methodologies and to investigate several aspects of forecast performance over time. ; The methodology assigns each forecast a composite score based on the standard theory of probability and statistics. This single number is easy to interpret and can be used to compare forecasts even if the number of variables being forecast, or their definitions, changes over time. ; The analysis shows that the Blue Chip Consensus Forecast, which is the average of the individual forecasts, performs better than any individual forecaster although several forecasters performed almost as well as the consensus.
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Bibliographic InfoArticle provided by Federal Reserve Bank of Atlanta in its journal Economic Review.
Volume (Year): (2003)
Issue (Month): Q2 ()
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- Marco Ottaviani & Peter Norman Sørensen, 2004.
"The Strategy of Professional Forecasting,"
FRU Working Papers
2004/05, University of Copenhagen. Department of Economics. Finance Research Unit.
- Robert Eisenbeis & Daniel Waggoner & Tao Zha, 2002. "Evaluating Wall Street Journal survey forecasters: a multivariate approach," Working Paper 2002-8, Federal Reserve Bank of Atlanta.
- Michael B. Devereux & Gregor W. Smith & James Yetman, 2009.
"Consumption and Real Exchange Rates in Professional Forecasts,"
NBER Working Papers
14795, National Bureau of Economic Research, Inc.
- Devereux, Michael B. & Smith, Gregor W. & Yetman, James, 2012. "Consumption and real exchange rates in professional forecasts," Journal of International Economics, Elsevier, vol. 86(1), pages 33-42.
- Michael B Devereux & Gregor W Smith & James Yetman, 2009. "Consumption and real exchange rates in professional forecasts," BIS Working Papers 295, Bank for International Settlements.
- Michael B. Devereux & Gregor W. Smith & James Yetman, 2009. "Consumption and Real Exchange Rates in Professional Forecasts," Working Papers 1195, Queen's University, Department of Economics.
- Spencer D. Krane, 2011. "Professional Forecasters' View of Permanent and Transitory Shocks to GDP," American Economic Journal: Macroeconomics, American Economic Association, vol. 3(1), pages 184-211, January.
- Frank A.G. den Butter & Pieter W. Jansen, 2008.
"Beating the Random Walk: a Performance Assessment of Long-term Interest Rate Forecasts,"
Tinbergen Institute Discussion Papers
08-102/3, Tinbergen Institute.
- Frank A. G. den Butter & Pieter W. Jansen, 2013. "Beating the random walk: a performance assessment of long-term interest rate forecasts," Applied Financial Economics, Taylor & Francis Journals, vol. 23(9), pages 749-765, May.
- Spencer Krane, 2006. "How professional forecasters view shocks to GDP," Working Paper Series WP-06-19, Federal Reserve Bank of Chicago.
- Berkelmans, Leon, 2011. "Imperfect information, multiple shocks, and policy's signaling role," Journal of Monetary Economics, Elsevier, vol. 58(4), pages 373-386.
- Carvalho, Fabia A. & Minella, André, 2012. "Survey forecasts in Brazil: A prismatic assessment of epidemiology, performance, and determinants," Journal of International Money and Finance, Elsevier, vol. 31(6), pages 1371-1391.
- Andrew Bauer & Robert A. Eisenbeis & Daniel F. Waggoner & Tao Zha, 2006.
"Transparency, expectations, and forecasts,"
2006-03, Federal Reserve Bank of Atlanta.
- Olivier Coibion & Yuriy Gorodnichenko, 2010.
"Information Rigidity and the Expectations Formation Process: A Simple Framework and New Facts,"
NBER Working Papers
16537, National Bureau of Economic Research, Inc.
- Olivier Coibion & Yuriy Gorodnichenko, 2010. "Information Rigidity and the Expectations Formation Process: A Simple Framework and New Facts," Working Papers 102, Department of Economics, College of William and Mary.
- Leon W. Berkelmans, 2008. "Imperfect information and monetary models: multiple shocks and their consequences," Finance and Economics Discussion Series 2008-58, Board of Governors of the Federal Reserve System (U.S.).
- Baghestani, Hamid, 2006. "An evaluation of the professional forecasts of U.S. long-term interest rates," Review of Financial Economics, Elsevier, vol. 15(2), pages 177-191.
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