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Wouter Van der Veken
- 3 December 2025
- OCCASIONAL PAPER SERIES - No. 381Details
- Abstract
- This report provides a comprehensive overview of the models and tools used for macroeconomic projections within the European System of Central Banks (ESCB). These include semi-structural models, dynamic stochastic general equilibrium (DSGE) models, time series models and specialised satellite models tailored to particular questions or country-specific aspects. Each type of model has its own strengths and weaknesses and can help answer different questions. The models should therefore be seen as complementary rather than mutually exclusive. Semi-structural models are commonly used to produce baseline projection exercises, since they offer the flexibility to combine expert judgement with empirical data and have enough complexity and structure to provide a good representation of the economy. DSGE models, valued for their internal consistency and strong theoretical foundations, are another core forecasting tool used by some central banks, particularly to analyse counterfactuals. Time series models tend to be better suited to forecasting the short term, while scenario analysis and special events may require satellite models, extensions of existing models or even the development of new models tailored to the question at hand. The report also addresses the challenges to macroeconomic projections posed by data quality, including revisions and missing data, and describes the methods implemented to mitigate their effects. The report identifies “quick wins” to improve the projection process by enhancing the transparency and comparability of results through standardised reporting frameworks and better measurement of the judgement integrated in forecasts. The findings highlight the fundamental role of macroeconomic models in underpinning the ESCB’s projection exercises and ensuring that the Governing Council’s assessments and deliberations rest on coherent, granular and credible analysis of both demand-side and supply-side dynamics.
- JEL Code
- C30 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→General
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
C54 : Mathematical and Quantitative Methods→Econometric Modeling→Quantitative Policy Modeling
E52 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Monetary Policy
- 22 September 2022
- WORKING PAPER SERIES - No. 2727Details
- Abstract
- What are the economic implications of financial and uncertainty shocks? We show that financial shocks cause a decline in output and goods prices, while uncertainty shocks cause a decline in output and an increase in goods prices. In response to un-certainty shocks, firms increase their markups, in line with the theory of self-insurance against being stuck with too low a price. This explains why goods prices may increase at the onset of a recession and are not accompanied by pronounced deflationary pressures. The two shocks are identified jointly with an approach that is less restrictive than Antolín-Díaz and Rubio-Ramírez’s method.
- JEL Code
- C32 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models, Diffusion Processes
E32 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Business Fluctuations, Cycles
- 11 September 2020
- WORKING PAPER SERIES - No. 2466Details
- Abstract
- We characterise the distribution of expected GDP growth during the Great Influenza Pandemic (known also as Spanish Flu) using a non-linear method in a country panel setting. We show that there are non-negligible risks of large GDP losses with the 5% left tail of the distribution suggesting a drop in the typical country's real per capita GDP equal to 29.1% in 1918, 10.9% in 1919 and 3.6% in 1920. Moreover, the fall in per capita GDP after the Spanish FLu was on average particularly large in low-income countries. Particularly, the size of the GDP drop in the lower tail of the distributions is high for higher income countries and immense for lower income countries. As for the United States, the estimated size of the recession in the lower tail of the distribution following the Spanish Flu is not negligible.
- JEL Code
- E3 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles
I0 : Health, Education, and Welfare→General
- 2 July 2020
- WORKING PAPER SERIES - No. 2436Details
- Abstract
- We show that financial variables contribute to the forecast of GDP growth during the Great Recession, providing additional insights on both first and higher moments of the GDP growth distribution. If a recession is due to an unforeseen shock (such as the Covid-19 recession), financial variables serve policymakers in providing timely warnings about the severity of the crisis and the macroeconomic risk involved, because downside risks increase as financial stress and corporate spreads become tighter. We use quantile regression and the skewed t-distribution and evaluate the forecasting properties of models using out-of-sample metrics with real-time vintages.
- JEL Code
- C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
E23 : Macroeconomics and Monetary Economics→Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy→Production
E27 : Macroeconomics and Monetary Economics→Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy→Forecasting and Simulation: Models and Applications
E32 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Business Fluctuations, Cycles
E44 : Macroeconomics and Monetary Economics→Money and Interest Rates→Financial Markets and the Macroeconomy