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Euro area real-time density forecasting with financial or labor market frictions

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  • McAdam, Peter
  • Warne, Anders

Abstract

We compare real-time density forecasts for the euro area using three DSGE models. The benchmark is the Smets-Wouters model and its forecasts of real GDP growth and inflation are compared with those from two extensions. The first adds financial frictions and expands the observables to include a measure of the external finance premium. The second allows for the extensive labor-market margin and adds the unemployment rate to the observables. The main question we address is if these extensions improve the density forecasts of real GDP and inflation and their joint forecasts up to an eight-quarter horizon. We find that adding financial frictions leads to a deterioration in the forecasts, with the exception of longer-term inflation forecasts and the period around the Great Recession. The labor market extension improves the medium to longer-term real GDP growth and shorter to medium-term inflation forecasts weakly compared with the benchmark model. JEL Classification: C11, C32, C52, C53, E37

Suggested Citation

  • McAdam, Peter & Warne, Anders, 2018. "Euro area real-time density forecasting with financial or labor market frictions," Working Paper Series 2140, European Central Bank.
    • Handle: RePEc:ecb:ecbwps:20182140
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    2. Yuri S. Popkov & Alexey Yu. Popkov & Yuri A. Dubnov & Dimitri Solomatine, 2020. "Entropy-Randomized Forecasting of Stochastic Dynamic Regression Models," Mathematics, MDPI, vol. 8(7), pages 1-20, July.
    3. Ganics, Gergely & Odendahl, Florens, 2021. "Bayesian VAR forecasts, survey information, and structural change in the euro area," International Journal of Forecasting, Elsevier, vol. 37(2), pages 971-999.
    4. Warne, Anders, 2023. "DSGE model forecasting: rational expectations vs. adaptive learning," Working Paper Series 2768, European Central Bank.
    5. Szabolcs Deak & Paul Levine & Afrasiab Mirza & Joseph Pearlman, 2020. "Is Price Level Targeting a Robust Monetary Rule?," Discussion Papers 20-27, Department of Economics, University of Birmingham.
    6. Gregory de Walque & Thomas Lejeune & Ansgar Rannenberg, 2023. "Empirical DSGE model evaluation with interest rate expectations measures and preferences over safe assets," Working Paper Research 433, National Bank of Belgium.
    7. McAdam, Peter & Warne, Anders, 2019. "Euro area real-time density forecasting with financial or labor market frictions," International Journal of Forecasting, Elsevier, vol. 35(2), pages 580-600.
    8. Ciccarelli, Matteo & Darracq Pariès, Matthieu & Priftis, Romanos & Angelini, Elena & Bańbura, Marta & Bokan, Nikola & Fagan, Gabriel & Gumiel, José Emilio & Kornprobst, Antoine & Lalik, Magdalena & Mo, 2024. "ECB macroeconometric models for forecasting and policy analysis," Occasional Paper Series 344, European Central Bank.
    9. McAdam, Peter & Warne, Anders, 2020. "Density forecast combinations: the real-time dimension," Working Paper Series 2378, European Central Bank.

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    More about this item

    Keywords

    Bayesian inference; DSGE models; forecast comparison; inflation; output; 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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