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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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Bibliographic Info

Article provided by American Statistical Association in its journal Journal of Business and Economic Statistics.

Volume (Year): 29 (2011)
Issue (Month): 3 ()
Pages: 397-410

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Handle: RePEc:bes:jnlbes:v:29:i:3:y:2011:p:397-410

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Cited by:
  1. Wojciech Charemza & Carlos Diaz & Svetlana Makarova, 2014. "Term Structure Of Inflation Forecast Uncertainties And Skew Normal Distributions," Discussion Papers in Economics 14/01, Department of Economics, University of Leicester.
  2. Berardi, Michele & Galimberti, Jaqueson K., 2014. "A note on the representative adaptive learning algorithm," Economics Letters, Elsevier, vol. 124(1), pages 104-107.

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