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Determinants of Multi-period Forecast Uncertainty Using a Panel of Density Forecasts

Author

Listed:
  • Fushang Liu
  • Kajal Lahiri
Abstract
This paper examines the determinants of inflation forecast uncertainty using a panel of density forecasts from the Survey of Professional Forecasters (SPF). We show that previous studies based on aggregate data are biased due to heterogeneity of individual forecasts. Instead, we estimate a dynamic heterogeneous panel data model. We find that, although past forecast uncertainty is important, it is not as important as previously thought. In addition, the strong link between past squared forecast errors and the current forecast uncertainty, as often is found in the GARCH literature, is largely lost in the multi-period context with varying forecast horizons. Forecasters are found to pay more attention to recent “news†about inflation than the out-dated past squared forecast errors. We propose a novel way to estimating uncertainty of “news†using Kullback-Leibler Information, and show that it is an important determinant of the current inflation forecast uncertainty. Our results also support Friedman (1977)’s conjecture that higher inflation rate leads to higher inflation uncertainty

Suggested Citation

  • Fushang Liu & Kajal Lahiri, 2004. "Determinants of Multi-period Forecast Uncertainty Using a Panel of Density Forecasts," Econometric Society 2004 Australasian Meetings 329, Econometric Society.
    • Handle: RePEc:ecm:ausm04:329
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    More about this item

    Keywords

    Forecast uncertainty; Heterogeneity of forecasts; Panel data; Survey of professional forecasters; Dynamic panels;
    All these keywords.

    JEL classification:

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

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