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Density forecast combinations: the real-time dimension

Author

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  • McAdam, Peter
  • Warne, Anders
Abstract
Density forecast combinations are examined in real-time using the log score to compare five methods: fixed weights, static and dynamic prediction pools, as well as Bayesian and dynamic model averaging. Since real-time data involves one vintage per time period and are subject to revisions, the chosen actuals for such comparisons typically differ from the information that can be used to compute model weights. The terms observation lag and information lag are introduced to clarify the different time shifts involved for these computations and we discuss how they influence the combination methods. We also introduce upper and lower bounds for the density forecasts, allowing us to benchmark the combination methods. The empirical study employs three DSGE models and two BVARs, where the former are variants of the Smets and Wouters model and the latter are benchmarks. The models are estimated on real-time euro area data and the forecasts cover 2001–2014, focusing on inflation and output growth. We find that some combinations are superior to the individual models for the joint and the output forecasts, mainly due to over-confident forecasts of the BVARs during the Great Recession. Combinations with limited weight variation over time and with positive weights on all models provide better forecasts than those with greater weight variation. For the inflation forecasts, the DSGE models are better overall than the BVARs and the combination methods. JEL Classification: C11, C32, C52, C53, E37

Suggested Citation

  • McAdam, Peter & Warne, Anders, 2020. "Density forecast combinations: the real-time dimension," Working Paper Series 2378, European Central Bank.
    • Handle: RePEc:ecb:ecbwps:20202378
    Note: 50336
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    1. Warne, Anders, 2023. "DSGE model forecasting: rational expectations vs. adaptive learning," Working Paper Series 2768, European Central Bank.

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    More about this item

    Keywords

    Bayesian inference; euro area; forecast comparisons; model averaging; prediction pools; predictive likelihood;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: 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
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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