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Stochastic Simulation of the FR-BDF Model and an Assessment of Uncertainty around Conditional Forecasts

Author

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  • Turunen Harry
  • Zhutova Anastasia
  • Lemoine Matthieu

Abstract

This paper presents a framework to introduce uncertainty into the FR-BDF model, used for macroeconomic projections and policy analysis at the Banque de France. Belonging to the semi-structural class of large-scale macroeconomic models, it is only fair to assume that FR-BDF may suffer from various types of misspecification. We do not seek to correct the latter, but instead we study its systematic nature using unobserved component models for the residuals of FR-BDF. Stochastic simulations based on random draws of innovations of these models allow us to work with applications that describe probabilities of events and risk in general. Applying this framework to the December 2022 forecast exercise of Banque de France, based on the available information at that time, the highest probability of observing a technical recession occurs in 2023Q2 and reaches 42%.

Suggested Citation

  • Turunen Harry & Zhutova Anastasia & Lemoine Matthieu, 2023. "Stochastic Simulation of the FR-BDF Model and an Assessment of Uncertainty around Conditional Forecasts," Working papers 920, Banque de France.
    • Handle: RePEc:bfr:banfra:920
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    References listed on IDEAS

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    1. Marta Banbura & Domenico Giannone & Lucrezia Reichlin, 2010. "Large Bayesian vector auto regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 71-92.
    2. Angelini, Elena & Bokan, Nikola & Christoffel, Kai & Ciccarelli, Matteo & Zimic, Srečko, 2019. "Introducing ECB-BASE: The blueprint of the new ECB semi-structural model for the euro area," Working Paper Series 2315, European Central Bank.
    3. Marta Banbura & Domenico Giannone & Lucrezia Reichlin, 2010. "Large Bayesian vector auto regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 71-92.
    4. Kadiyala, K Rao & Karlsson, Sune, 1997. "Numerical Methods for Estimation and Inference in Bayesian VAR-Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(2), pages 99-132, March-Apr.
    5. Manuel Gonzalez-Astudillo & Diego Vilán, 2019. "A New Procedure for Generating the Stochastic Simulations in FRB/US," FEDS Notes 2019-03-07, Board of Governors of the Federal Reserve System (U.S.).
    6. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2016. "Large Vector Autoregressions with Stochastic Volatility and Flexible Priors," Working Papers (Old Series) 1617, Federal Reserve Bank of Cleveland.
    7. Angelini, Elena & Lalik, Magdalena & Lenza, Michele & Paredes, Joan, 2019. "Mind the gap: A multi-country BVAR benchmark for the Eurosystem projections," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1658-1668.
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    More about this item

    Keywords

    Semi-Structural Modelling; Stochastic Simulation; Unobserved Component Model;
    All these keywords.

    JEL classification:

    • C54 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Quantitative Policy Modeling
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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