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ARCH models for multi-period forecast uncertainty-a reality check using a panel of density forecasts

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  • Lahiri, Kajal
  • Liu, Fushang

Abstract

We develop a theoretical framework to compare forecast uncertainty estimated from time series models to those available from survey density forecasts. The sum of the average variance of individual densities and the disagreement, which is the same as the variance of the aggregate density, is shown to approximate the predictive uncertainty from well specified time series models when the variance of the aggregate shocks is relatively small compared to that of the idiosyncratic shocks. We argue that due to grouping error problems, compositional effects of the panel, and other complications, the uncertainty measure has to be estimated from individual densities. Despite numerous reservations on the credibility of time series based measures of forecast uncertainty, we found that during normal times the uncertainty estimates based on ARCH models simulate the subjective survey measure remarkably well. However, during times of regime change and structural break, the two estimates do not overlap. We suggest ways to improve the time series measures during periods when forecast errors are apt to be large. The disagreement series is a good indicator of such periods.

Suggested Citation

  • Lahiri, Kajal & Liu, Fushang, 2005. "ARCH models for multi-period forecast uncertainty-a reality check using a panel of density forecasts," MPRA Paper 21693, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:21693
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    References listed on IDEAS

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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. 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.
    3. Hartmann, Matthias & Herwartz, Helmut & Ulm, Maren, 2017. "A comparative assessment of alternative ex ante measures of inflation uncertainty," International Journal of Forecasting, Elsevier, vol. 33(1), pages 76-89.
    4. Glas, Alexander & Hartmann, Matthias, 2016. "Inflation uncertainty, disagreement and monetary policy: Evidence from the ECB Survey of Professional Forecasters," Working Papers 0612, University of Heidelberg, Department of Economics.
    5. Christian Grimme & Steffen Henzel & Elisabeth Wieland, 2014. "Inflation uncertainty revisited: a proposal for robust measurement," Empirical Economics, Springer, vol. 47(4), pages 1497-1523, December.
    6. Fushang Liu & Kajal Lahiri, 2006. "Modelling multi-period inflation uncertainty using a panel of density forecasts," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(8), pages 1199-1219.
    7. Glas, Alexander & Hartmann, Matthias, 2016. "Inflation uncertainty, disagreement and monetary policy: Evidence from the ECB Survey of Professional Forecasters," Annual Conference 2016 (Augsburg): Demographic Change 145888, Verein für Socialpolitik / German Economic Association.

    More about this item

    Keywords

    Inflation; Survey of Professional Forecasters; GARCH; Real time data.;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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