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Professional Forecasters' View of Permanent and Transitory Shocks to GDP

Author

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  • Spencer D. Krane

Abstract

This paper examines how the professional forecasters comprising the Blue Chip Economic Consensus view shocks to GDP. I use an unobserved components model of the forecast revisions to identify forecasters' perceptions of permanent and transitory shocks to GDP. The model indicates forecasters: attribute about two-thirds of the variance in current-period revisions to permanent shocks; view the relative importance of permanent shocks similar to the estimates of some simple univariate econometric models; see high-frequency indicators of economic activity as being informative about both permanent and transitory shocks; and react to incoming data differently during periods of economic weakness. (JEL C51, C53, E23, E27, E32, E37)

Suggested Citation

  • 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.
  • Handle: RePEc:aea:aejmac:v:3:y:2011:i:1:p:184-211
    Note: DOI: 10.1257/mac.3.1.184
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    References listed on IDEAS

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    1. Blanchard, Olivier Jean & Quah, Danny, 1989. "The Dynamic Effects of Aggregate Demand and Supply Disturbances," American Economic Review, American Economic Association, vol. 79(4), pages 655-673, September.
    2. Beveridge, Stephen & Nelson, Charles R., 1981. "A new approach to decomposition of economic time series into permanent and transitory components with particular attention to measurement of the `business cycle'," Journal of Monetary Economics, Elsevier, vol. 7(2), pages 151-174.
    3. Giannone, Domenico & Reichlin, Lucrezia & Small, David, 2008. "Nowcasting: The real-time informational content of macroeconomic data," Journal of Monetary Economics, Elsevier, vol. 55(4), pages 665-676, May.
    4. 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.
    5. Andrew 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.
    6. Edge, Rochelle M. & Laubach, Thomas & Williams, John C., 2007. "Learning and shifts in long-run productivity growth," Journal of Monetary Economics, Elsevier, vol. 54(8), pages 2421-2438, November.
    7. Charles L. Evans & Chin Te Liu & Genevieve Pham-Kanter, 2002. "The 2001 recession and the Chicago Fed National Index: identifying business cycle turning points," Economic Perspectives, Federal Reserve Bank of Chicago, issue Q III, pages 26-43.
    8. Spencer D. Krane, 2003. "An evaluation of real GDP forecasts: 1996-2001," Economic Perspectives, Federal Reserve Bank of Chicago, issue Q I, pages 2-21.
    9. Marc P. Giannoni & Jean Boivin, 2005. "DSGE Models in a Data-Rich Environment," Computing in Economics and Finance 2005 431, Society for Computational Economics.
    10. Giannone, Domenico & Reichlin, Lucrezia & Small, David, 2005. "Nowcasting GDP and Inflation: The Real Time Informational Content of Macroeconomic Data Releases," CEPR Discussion Papers 5178, C.E.P.R. Discussion Papers.
    11. Watson, Mark W., 1986. "Univariate detrending methods with stochastic trends," Journal of Monetary Economics, Elsevier, vol. 18(1), pages 49-75, July.
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    14. 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.
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    Citations

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    Cited by:

    1. Monica Jain, 2013. "Perceived Inflation Persistence," Staff Working Papers 13-43, Bank of Canada.
    2. James M. Nason & Gregor W. Smith, 2013. "Reverse Kalman filtering U.S. inflation with sticky professional forecasts," Working Papers 13-34, Federal Reserve Bank of Philadelphia.
    3. James M. Nason & Gregor W. Smith, 2013. "Measuring the Slowly Evolving Trend in US Inflation with Professional Forecasts," Working Papers 1316, Queen's University, Department of Economics.
    4. Chauvet, Marcelle & Potter, Simon, 2013. "Forecasting Output," Handbook of Economic Forecasting, Elsevier.

    More about this item

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E23 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Production
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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