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How professional forecasters view shocks to GDP

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  • Spencer Krane

Abstract

Economic activity depends on agents' real-time beliefs regarding the persistence in the shocks they currently perceive to be hitting the economy. This paper uses an unobserved components model of forecast revisions to examine how the professional forecasters comprising the Blue Chip Economic Consensus have viewed such shocks to GDP over the past twenty years. The model estimates that these forecasters attribute more of the variance in the shock to GDP to permanent factors than to transitory developments. Both shocks are significantly correlated with incoming high-frequency indicators of economic activity; but for the permanent component, the correlation is driven by recessions or other periods when activity was weak. The forecasters' shocks also differ noticeably from those generated by some simple econometric models. Taken together, the results suggest that agents? expectations likely are based on broader information sets than those used to specify most empirical models and that the mechanisms generating expectations may differ with the perceived state of the business cycle.

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Bibliographic Info

Paper provided by Federal Reserve Bank of Chicago in its series Working Paper Series with number WP-06-19.

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Date of creation: 2006
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Handle: RePEc:fip:fedhwp:wp-06-19

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Keywords: Gross domestic product;

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  1. Ang, Andrew & Piazzesi, Monika, 2003. "A no-arbitrage vector autoregression of term structure dynamics with macroeconomic and latent variables," Journal of Monetary Economics, Elsevier, vol. 50(4), pages 745-787, May.
  2. Andy 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, issue Q2, pages 17-31.
  3. Campbell, John & Mankiw, Gregory, 1987. "Are Output Fluctuations Transitory?," Scholarly Articles 3122545, Harvard University Department of Economics.
  4. Berger, Allen N & Krane, Spencer D, 1985. "The Information Efficiency of Econometric Model Forecasts," The Review of Economics and Statistics, MIT Press, vol. 67(1), pages 128-34, February.
  5. Rochelle M. Edge & Thomas Laubach & John C. Williams, 2004. "Learning and shifts in long-run productivity growth," Working Papers in Applied Economic Theory 2004-04, Federal Reserve Bank of San Francisco.
  6. Clark, Peter K, 1987. "The Cyclical Component of U.S. Economic Activity," The Quarterly Journal of Economics, MIT Press, vol. 102(4), pages 797-814, November.
  7. Campbell, John Y & Mankiw, N Gregory, 1987. "Permanent and Transitory Components in Macroeconomic Fluctuations," American Economic Review, American Economic Association, vol. 77(2), pages 111-17, May.
  8. Watson, Mark W., 1986. "Univariate detrending methods with stochastic trends," Journal of Monetary Economics, Elsevier, vol. 18(1), pages 49-75, July.
  9. Cochrane, John H, 1988. "How Big Is the Random Walk in GNP?," Journal of Political Economy, University of Chicago Press, vol. 96(5), pages 893-920, October.
  10. Matthew D. Shapiro & Mark W. Watson, 1989. "Sources of Business Cycle Fluctuations," NBER Working Papers 2589, National Bureau of Economic Research, Inc.
  11. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-84, March.
  12. Nelson, Charles R. & Plosser, Charles I., 1982. "Trends and random walks in macroeconmic time series : Some evidence and implications," Journal of Monetary Economics, Elsevier, vol. 10(2), pages 139-162.
  13. Cochrane, John H, 1994. "Permanent and Transitory Components of GNP and Stock Prices," The Quarterly Journal of Economics, MIT Press, vol. 109(1), pages 241-65, February.
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