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From Fixed-Event to Fixed-Horizon Density Forecasts: Obtaining Measures of Multi-Horizon Uncertainty from Survey Density Forecasts

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  • Gergely Ganics
  • Barbara Rossi
  • Tatevik Sekhposyan

Abstract

Surveys of professional forecasters produce precise and timely point forecasts for key macroeconomic variables. However, the accompanying density forecasts are not as widely utilized, and there is no consensus about their quality. This is partly because such surveys are often conducted for “fixed events”. For example, in each quarter panelists are asked to forecast output growth and inflation for the current calendar year and the next, implying that the forecast horizon changes with each survey round. The fixed-event nature limits the usefulness of survey density predictions for policymakers and market participants, who often wish to characterize uncertainty a fixed number of periods ahead (“fixed-horizon”). Is it possible to obtain fixed-horizon density forecasts using the available fixed-event ones? We propose a density combination approach that weights fixed-event density forecasts according to a uniformity of the probability integral transform criterion, aiming at obtaining a correctly calibrated fixed-horizon density forecast. Using data from the US Survey of Professional Forecasters, we show that our combination method produces competitive density forecasts relative to widely used alternatives based on historical forecast errors or Bayesian VARs. Thus, our proposed fixed-horizon predictive densities are a new and useful tool for researchers and policy makers.

Suggested Citation

  • Gergely Ganics & Barbara Rossi & Tatevik Sekhposyan, 2020. "From Fixed-Event to Fixed-Horizon Density Forecasts: Obtaining Measures of Multi-Horizon Uncertainty from Survey Density Forecasts," Working Papers 1142, Barcelona Graduate School of Economics.
  • Handle: RePEc:bge:wpaper:1142
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    References listed on IDEAS

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    Cited by:

    1. De Santis, Roberto A. & Van der Veken, Wouter, 2020. "Forecasting macroeconomic risk in real time: Great and Covid-19 Recessions," Working Paper Series 2436, European Central Bank.

    More about this item

    Keywords

    survey of professional forecasters; density forecasts; forecast combination; predictive density; probability integral transform; uncertainty; real-time;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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