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Predictability of Output Growth and Inflation: A Multi-Horizon Survey Approach

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  • Patton, Andrew J.
  • Timmermann, Allan

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 in the observables. Empirical estimates of the model are obtained using survey forecasts of annual GDP growth and inflation in the United States with forecast horizons ranging from 1 to 24 months, and the model is found to closely match the joint realization of forecast errors at different horizons. Our empirical results suggest that professional forecasters face severe measurement error problems for GDP growth in real time, while this is much less of a problem for inflation. Moreover, inflation exhibits greater persistence, and thus is predictable at longer horizons, than GDP growth and the persistent component of both variables is well approximated by a low-order autoregressive specification.
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Suggested Citation

  • Patton, Andrew J. & Timmermann, Allan, 2011. "Predictability of Output Growth and Inflation: A Multi-Horizon Survey Approach," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(3), pages 397-410.
  • Handle: RePEc:bes:jnlbes:v:29:i:3:y:2011:p:397-410
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    Cited by:

    1. Atalla, Tarek & Joutz, Fred & Pierru, Axel, 2016. "Does disagreement among oil price forecasters reflect volatility? Evidence from the ECB surveys," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1178-1192.
    2. Wojciech Charemza & Carlos Diaz Vela & Svetlana Makarova, 2013. "Inflation fan charts, monetary policy and skew normal distribution," Discussion Papers in Economics 13/06, Department of Economics, University of Leicester.
    3. Joscha Beckmann & Robert L. Czudaj, 2018. "Monetary Policy Shocks, Expectations, And Information Rigidities," Economic Inquiry, Western Economic Association International, vol. 56(4), pages 2158-2176, October.
    4. Clements, Michael P., 2012. "Subjective and Ex Post Forecast Uncertainty: US Inflation and Output Growth," Economic Research Papers 270629, University of Warwick - Department of Economics.
    5. repec:eee:intfor:v:33:y:2017:i:3:p:591-604 is not listed on IDEAS
    6. Kajal Lahiri & Yongchen Zhao, 2018. "International Propagation of Shocks: A Dynamic Factor Model Using Survey Forecasts," Working Papers 2018-04, Towson University, Department of Economics, revised Sep 2018.
    7. Knüppel, Malte, 2018. "Forecast-error-based estimation of forecast uncertainty when the horizon is increased," International Journal of Forecasting, Elsevier, vol. 34(1), pages 105-116.
    8. Michael P Clements, 2014. "Assessing the Evidence of Macro- Forecaster Herding: Forecasts of Inflation and Output Growth," ICMA Centre Discussion Papers in Finance icma-dp2014-12, Henley Business School, Reading University.
    9. Czudaj, Robert & Beckmann, Joscha, 2018. "Monetary policy shocks, expectations and information rigidities," Annual Conference 2018 (Freiburg, Breisgau): Digital Economy 181573, Verein für Socialpolitik / German Economic Association.
    10. Berardi, Michele & Galimberti, Jaqueson K., 2014. "A note on the representative adaptive learning algorithm," Economics Letters, Elsevier, vol. 124(1), pages 104-107.
    11. Bruno Deschamps & Christos Ioannidis, 2014. "The Efficiency of Multivariate Macroeconomic Forecasts," Manchester School, University of Manchester, vol. 82(5), pages 509-523, September.

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