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The Resolution of Macroeconomic Uncertainty: Evidence from Survey Forecast

Author

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  • 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.

Suggested Citation

  • Andrew J. Patton & Allan Timmermann, 2008. "The Resolution of Macroeconomic Uncertainty: Evidence from Survey Forecast," CREATES Research Papers 2008-54, Department of Economics and Business Economics, Aarhus University.
  • Handle: RePEc:aah:create:2008-54
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    References listed on IDEAS

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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. Jonas Dovern & Ulrich Fritsche & Jiri Slacalek, 2012. "Disagreement Among Forecasters in G7 Countries," The Review of Economics and Statistics, MIT Press, vol. 94(4), pages 1081-1096, November.
    3. repec:sbe:breart:v:35:y:2015:i:1:a:17002 is not listed on IDEAS
    4. Ohnsorge,Franziska Lieselotte & Stocker,Marc & Some,Modeste Y., 2016. "Quantifying uncertainties in global growth forecasts," Policy Research Working Paper Series 7770, The World Bank.

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    Keywords

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

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