An Update of FR-BDF Basic Model Elasticities and the FR-BMEs Model: A Simplified Forecasting Platform for Banque de France Macroeconomic Interim Projections
Working Paper Series no. 1045. This paper has two objectives. First, it updates the set of Basic Model Elasticities (BMEs) of the Banque de France's semi-structural macroeconomic model for France (FR-BDF), initially presented in 2020. Since then, FR-BDF has been enhanced with detailed blocks for household credit and housing markets, corporate financing, and fully re-estimated following the release of national accounts in base 2020. The BMEs reported here reflect these improvements and remain constructed under an orthogonality principle, providing analytical responses to isolated shocks that can be combined to design alternative scenarios. Second, the paper introduces FR-BMEs, a simplified, linearized model based on FR-BDF BMEs, which has been used since March 2024 for interim projections. While this approach improves efficiency, it reduces the granularity of projections, notably for public finances and household income. FR-BMEs also supports policy analysis, including model-based forecast-error decomposition. We illustrate this with a post-mortem of the 2024 GDP growth forecast error from the December 2023 Banque de France's projection. Actual growth reached 1.1 % versus a forecast of 0.9 %, implying a +0.2pp error. This discrepancy mainly reflects historical data revisions (+0.16pp) and, to a lesser extent, changes in Eurosystem assumptions (+0.05pp). By coincidence, revisions in public finance assumptions (+0.28 pp) and errors in expert judgement (- 0.28pp) offset each other.
Figure 1. Breakdown of 2024 real GDP growth forecast error (observed data-forecast)
Non-Technical Summary
This paper presents two developments in the macroeconomic modelling tools used by the Banque de France for forecasting and policy analysis.
The first is an updated set of Basic Model Elasticities (BMEs), which describe how the macroeconomic model FR BDF reacts to a range of standardized shocks. BMEs are constructed according to an orthogonality principle, meaning that only one variable is perturbed at a time and that only the endogenous core of the model remains activated. This allows the elasticities to be interpreted as analytical building blocks: their effects can be combined additively to construct alternative scenarios or to explore the transmission of different types of shocks. These elasticities have been recalculated following the full re estimation of the FR-BDF model in 2024, a process that incorporated the national accounts rebased to 2020 and integrated new modelling blocks, including household credit and housing, as well as corporate financing.
The second contribution is the introduction of FR BMEs, a simplified and linearised forecasting platform derived directly from these elasticities. Since March 2024, FR BMEs has been used for the Banque de France’s interim projection exercises conducted in March and September, while the full FR BDF model remains the benchmark for the semi annual Eurosystem Broad Macroeconomic Projection Exercises. The simplified model substantially improves operational efficiency during interim projection rounds, in which most revisions stem from updated data and changes in technical or fiscal assumptions. This gain in efficiency, however, comes with certain trade offs, as FR BMEs does not generate detailed projections for public finances or for household income, savings, and corporate margins. Nonetheless, FR BMEs remains fully consistent with the dynamics of FR BDF and is well suited both for forecasting and for counterfactual analysis.
The paper illustrates the usefulness of this framework through a model based decomposition of the 2024 real GDP growth forecast error associated with the December 2023 projection. Actual GDP growth reached 1.1 percent in 2024, compared with a forecast of 0.9 percent. Using FR BMEs, the paper decomposes this 0.2 percentage point forecast error into distinct and interpretable components. Revisions to historical national accounts data account for most of the discrepancy, contributing +0.16 percentage point, through an upward revision of the 2024 carry over. Updated international and technical assumptions contribute +0.05 percentage point, with small and offsetting errors in foreign demand, energy prices, and interest rates. Public finance assumptions—particularly regarding public investment, employment, and tax revenues—contribute +0.28 percentage point. These factors are offset almost entirely by expert judgement errors, which subtract –0.28 percentage point and stem largely from unexpected developments in domestic demand and imports.
Overall, the paper illustrates how the BMEs and the FR BMEs model enhance transparency and analytical clarity in the forecasting process. They make it possible to translate changes in assumptions or incoming data into a coherent macroeconomic projection, to disentangle the different origins of forecast errors, and to provide policymakers with internally consistent and readily interpretable scenarios for the French economy.
Keywords: Semi-Structural Modelling, Macroeconomic Forecasting, Macroeconomic Policy Analysis
Codes JEL : E17, E6
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Updated on the 13th of May 2026