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The Resolution of Macroeconomic Uncertainty: Evidence from Survey Forecast

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Author Info

  • Andrew J. Patton
  • Allan Timmermann

    ()
    (School of Economics and Management, University of Aarhus, Denmark and CREATES)

Abstract

We develop an unobserved components approach to study surveys of forecasts containing multiple forecast horizons. Under the assumption that forecasters optimally update their beliefs about past, current and future state variables as new information arrives, we use our model to extract information on the degree of predictability of the state variable and the importance of measurement errors on that variable. Empirical estimates of the model are obtained using survey forecasts of annual GDP growth and inflation in the US with forecast horizons ranging from 1 to 24 months. The model is found to closely match the joint realization of forecast errors at different horizons and is used to demonstrate how uncertainty about macroeconomic variables is resolved.

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Bibliographic Info

Paper provided by School of Economics and Management, University of Aarhus in its series CREATES Research Papers with number 2008-54.

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Length: 34
Date of creation: 19 Sep 2008
Date of revision:
Handle: RePEc:aah:create:2008-54

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Web page: http://www.econ.au.dk/afn/

Related research

Keywords: Fixed-event forecasts; multiple forecast horizons; Kalman filtering; survey data;

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References

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  1. Isiklar, Gultekin & Lahiri, Kajal & Loungani, Prakash, 2006. "How quickly do forecasters incorporate news? Evidence from cross-country surveys," MPRA Paper 22065, University Library of Munich, Germany.
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  5. 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.
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  7. Lavan Mahadeva & Alex Muscatelli, 2005. "National Accounts Revisions and Output Gap Estimates in a Model of Monetary Policy with Data Uncertainty," Discussion Papers 14, Monetary Policy Committee Unit, Bank of England.
  8. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 33(1), pages 125-132.
  9. Croushore, Dean, 2006. "Forecasting with Real-Time Macroeconomic Data," Handbook of Economic Forecasting, Elsevier.
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  13. Patton, Andrew J. & Timmermann, Allan, 2007. "Testing Forecast Optimality Under Unknown Loss," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1172-1184, December.
  14. Stock, James H. & Watson, Mark W., 2006. "Forecasting with Many Predictors," Handbook of Economic Forecasting, Elsevier.
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  17. Swidler, Steve & Ketcher, David, 1990. "Economic Forecasts, Rationality, and the Processing of New Information over Time," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 22(1), pages 65-76, February.
  18. Dean Croushore & Tom Stark, 1999. "A real-time data set for macroeconomists," Working Papers 99-4, Federal Reserve Bank of Philadelphia.
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Cited by:
  1. Pfajfar, D. & Zakelj, B., 2012. "Uncertainty and Disagreement in Forecasting Inflation: Evidence from the Laboratory (Revised version of CentER DP 2011-053)," Discussion Paper 2012-072, Tilburg University, Center for Economic Research.
  2. Dovern, Jonas & Fritsche, Ulrich & Slacalek, Jiri, 2009. "Disagreement among forecasters in G7 countries," Working Paper Series 1082, European Central Bank.

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