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Information Rigidities in Economic Growth Forecasts: Evidence from a Large International Panel

  • Dovern, Jonas
  • Fritsche, Ulrich
  • Loungani, Prakash
  • Tamirisa, Natalia

We examine the behavior of forecasts for real GDP growth using a large panel of individual forecasts from 30 advanced and emerging economies during 1989-2010. Our main findings are as follows. First, our evidence does not support the validity of the sticky information model (Mankiw and Reis, 2002) for describing the dynamics of professional growth forecasts. Instead, the empirical evidence is more in line with implications of "noisy" information models (Woodford, 2002; Sims, 2003). Second, we find that information rigidities are more pronounced in emerging economies than advanced economies. Third, there is evidence of nonlinearities in forecast smoothing. It is less pronounced in the tails of the distribution of individual forecast revisions than in the central part of the distribution.

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Paper provided by Verein für Socialpolitik / German Economic Association in its series Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order with number 79936.

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Date of creation: 2013
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Handle: RePEc:zbw:vfsc13:79936
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  1. Jörg Döpke & Jonas Dovern & Ulrich Fritsche & Jiri Slacalek, 2008. "Sticky Information Phillips Curves: European Evidence," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 40(7), pages 1513-1520, October.
  2. Yuriy Gorodnichenko & Olivier Coibion, 2010. "What can survey forecasts tell us about informational rigidities?," 2010 Meeting Papers 277, Society for Economic Dynamics.
  3. Olivier Coibion & Yuriy Gorodnichenko, 2015. "Information Rigidity and the Expectations Formation Process: A Simple Framework and New Facts," American Economic Review, American Economic Association, vol. 105(8), pages 2644-78, August.
  4. Ager, Philipp & Kappler, Marcus & Osterloh, Steffen, 2007. "The Accuracy and Efficiency of the Consensus Forecasts: A Further Application and Extension of the Pooled Approach," ZEW Discussion Papers 07-058, ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.
  5. Christopher W. Crowe, 2010. "Consensus Forecasts and Inefficient Information Aggregation," IMF Working Papers 10/178, International Monetary Fund.
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  7. N. Gregory Mankiw & Ricardo Reis, 2001. "Sticky Information Versus Sticky Prices: A Proposal to Replace the New Keynesian Phillips Curve," Harvard Institute of Economic Research Working Papers 1922, Harvard - Institute of Economic Research.
  8. Arellano, Manuel & Bover, Olympia, 1995. "Another look at the instrumental variable estimation of error-components models," Journal of Econometrics, Elsevier, vol. 68(1), pages 29-51, July.
  9. Jonas Dovern & Ulrich Fritsche & Jiri Slacalek, 2009. "Disagreement among Forecasters in G7 Countries," Macroeconomics and Finance Series 200906, Hamburg University, Department Wirtschaft und Politik.
  10. Jonas Dovern & Johannes Weisser, 2009. "Accuracy, Unbiasedness and Efficiency of Professional Macroeconomic Forecasts: An empirical Comparison for the G7," Jena Economic Research Papers 2009-091, Friedrich-Schiller-University Jena, Max-Planck-Institute of Economics.
  11. Nickell, Stephen J, 1981. "Biases in Dynamic Models with Fixed Effects," Econometrica, Econometric Society, vol. 49(6), pages 1417-26, November.
  12. Kajal Lahiri & Gultekin Isiklar, 2006. "How Far Ahead Can We Forecast? Evidence From Cross-country Surveys," Discussion Papers 06-04, University at Albany, SUNY, Department of Economics.
  13. Loungani, Prakash, 2001. "How accurate are private sector forecasts? Cross-country evidence from consensus forecasts of output growth," International Journal of Forecasting, Elsevier, vol. 17(3), pages 419-432.
  14. Arellano, Manuel & Bond, Stephen, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," Review of Economic Studies, Wiley Blackwell, vol. 58(2), pages 277-97, April.
  15. Roy Batchelor, 2007. "Forecaster Behaviour and Bias in Macroeconomic Forecasts," Ifo Working Paper Series Ifo Working Paper No. 39, Ifo Institute for Economic Research at the University of Munich.
  16. Lahiri, Kajal & Sheng, Xuguang, 2008. "Evolution of forecast disagreement in a Bayesian learning model," Journal of Econometrics, Elsevier, vol. 144(2), pages 325-340, June.
  17. Khan, Hashmat & Zhu, Zhenhua, 2006. "Estimates of the Sticky-Information Phillips Curve for the United States," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 38(1), pages 195-207, February.
  18. Davies, Anthony & Lahiri, Kajal, 1995. "A new framework for analyzing survey forecasts using three-dimensional panel data," Journal of Econometrics, Elsevier, vol. 68(1), pages 205-227, July.
  19. Batchelor, Roy, 2007. "Bias in macroeconomic forecasts," International Journal of Forecasting, Elsevier, vol. 23(2), pages 189-203.
  20. John C. Driscoll & Aart C. Kraay, 1998. "Consistent Covariance Matrix Estimation With Spatially Dependent Panel Data," The Review of Economics and Statistics, MIT Press, vol. 80(4), pages 549-560, November.
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