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Modeling Multi-Period Inflation Uncertainty Using a Panel of Density Forcasts

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  • Kajal Lahiri
  • Fushang Liu

Abstract

This paper examines the determinants of inflation forecast uncertainty using a panel of density forecasts from the Survey of Professional Forecasters (SPF). Based on a dynamic heterogeneous panel data model, we find that the persistence in forecast uncertainty is much less than what the aggregate time series data would suggest. In addition, the strong link between past forecast errors and current forecast uncertainty, as often is noted in the ARCH literature, is largely lost in a multiperiod context with varying forecast horizons. We propose a novel way of estimating ¡°news¡± and its variance using the Kullback-Leibler Information, and show that the latter is an important determinant of forecast uncertainty. Our evidence suggests a strong relationship of forecast uncertainty with level of inflation, but not with forecaster discord or with the volatility of a number of other macroeconomic indicators.

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Paper provided by University at Albany, SUNY, Department of Economics in its series Discussion Papers with number 06-05.

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Date of creation: 2006
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Handle: RePEc:nya:albaec:06-05

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Postal: Department of Economics, BA 110 University at Albany State University of New York Albany, NY 12222 U.S.A.
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Cited by:
  1. Knüppel, Malte, 2014. "Efficient estimation of forecast uncertainty based on recent forecast errors," International Journal of Forecasting, Elsevier, vol. 30(2), pages 257-267.
  2. Baltagi, Badi H., 2006. "Forecasting with panel data," Discussion Paper Series 1: Economic Studies 2006,25, Deutsche Bundesbank, Research Centre.
  3. 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.
  4. Halina Kowalczyk & Tomasz Lyziak & Ewa Stanisławska, 2013. "A new approach to probabilistic surveys of professional forecasters and its application in the monetary policy context," National Bank of Poland Working Papers 142, National Bank of Poland, Economic Institute.
  5. Carrera Cesar, 2012. "Estimating Information Rigidity Using Firms' Survey Data," The B.E. Journal of Macroeconomics, De Gruyter, vol. 12(1), pages 1-34, June.
  6. Kajal Lahiri & Fushang Liu, 2009. "On the Use of Density Forecasts to Identify Asymmetry in Forecasters' Loss Functions," Discussion Papers 09-03, University at Albany, SUNY, Department of Economics.
  7. Kajal Lahiri & Xuguang Sheng, 2010. "Measuring forecast uncertainty by disagreement: The missing link," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 514-538.
  8. 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.
  9. Stefania D'Amico & Athanasios Orphanides, 2008. "Uncertainty and disagreement in economic forecasting," Finance and Economics Discussion Series 2008-56, Board of Governors of the Federal Reserve System (U.S.).

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