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The main elements of my curriculum vitae are provided below. A more complete PDF version is available for download.
Basics
| Name | Sylvain Barde |
| Label | Computational Economist |
| s.barde@kent.ac.uk | |
| Phone | +44 1 227 824 092 |
| Url | https://sylvain-barde.github.io/ |
Work
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2010.09 - Current -
2010.08 - 2007.09 Chargé d'Etudes (Research Economist)
Observatoire Français des Conjonctures Economiques
Research analyst working on economic geography, innovation diffusion and industrial policy
Education
Awards
- 2001.2002
Chevening Scholar
Foreign, Commonwealth and Development Office, UK
Chevening scholarships are the UK government's global scholarship programme, funded by the Foreign, Commonwealth and Development Office (FCDO), and are awarded to outstanding scholars with leadership potential.
Publications
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2026.05.09 Bayesian Estimation of a Large-Scale Macroeconomic Policy Agent-Based Model
Journal of Economic Dynamics and Control
Empirical parameter estimation of large scale agent-based models has long been recognised as computationally challenging. Their bottom-up nature imposes the use of nonparametric or indirect inference methods, which in turn typically requires a significant amount simulated data. However, their high computational requirements makes the application of these estimation methodologies unfeasible in practice. This hurdle can limit the applicability of agent based models for quantitative policy advice, for example in scenario analysis, in cases where the parameter calibration cannot be checked against empirical data. We show how this problem can be overcome by estimating the Dosi et al. (2015) agent-based model. This extends the original ‘Keynes meets Schumpeter’ model of Dosi et al. (2010) allowing for the interaction of fiscal and monetary policy. 18 free parameters of the model are estimated on 10 standard macroeconomic US variables, using annual and quarterly data, and the estimates obtained are shown to improve the fit of the baseline model on the data. A model-selection exercise is carried out, investigating impact of changing the expectation-formation mechanism. Finally, the original policy experiments are replicated using the new empirical estimates, showing that pushing the model into a low-growth regime leads to several key differences relative to the original conclusions. Overall, the exercise establishes the feasibility and relevance of macro-economic empirical parameter estimation and mechanism selection to ABM designers for improving the fit of scenario analyses.
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2026.01.01 Large-Scale Model Comparison with Fast Model Confidence Sets
Journal of Econometrics
The paper proposes a new algorithm for finding the confidence set of a collection of forecasts or prediction models. Existing numerical implementations use an elimination approach, where one starts with the full collection of models and successively eliminates the worst performing until the null of equal predictive ability is no longer rejected at a given confidence level. The intuition behind the proposed implementation lies in reversing the process, i.e. starting with a collection of two models and updating both the model rankings and p-values as models are successively added to the collection. The first benefit of this approach is a reduction of one polynomial order in both the time complexity and memory cost of finding the confidence set of a collection of M models using the R rule, falling respectively from O(M^3) to O(M^2) and from O(M^2) to O(M). The second key benefit is that it allows for further models to be added at a later point in time, thus enabling collaborative efforts using the model confidence set procedure. We prove that this implementation is equivalent to the elimination approach and demonstrate the improved performance on a multivariate GARCH collection consisting of 4800 models.
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2025.02.27 Moran’s I Lasso for models with spatially correlated data
The Econometrics Journal
This paper proposes a Lasso-based estimator which uses information embedded in the Moran statistic to develop a selection procedure called Moran’s I Lasso (Mi-Lasso) to solve the Eigenvector Spatial Filtering (ESF) eigenvector selection problem. ESF uses a subset of eigenvectors from a spatial weights matrix to efficiently account for any omitted spatially correlated terms in a classical linear regression framework, thus eliminating the need for the researcher to explicitly specify the spatially correlated parts of the model. We proposed the first ESF procedure accounting for post-selection inference. We derive performance bounds and show the necessary conditions for consistent eigenvector selection. The key advantages of the proposed estimator are that it is intuitive, theoretically grounded, able to provide robust inference and substantially faster than Lasso based on cross-validation or any proposed forward stepwise procedure. Our simulation results and an application on house prices demonstrate Mi-Lasso performs well compared to existing procedures in finite samples.
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2024.04.23 Bayesian estimation of large-scale simulation models with Gaussian process regression surrogates
Computational Statistics and Data Analysis
This paper develops a likelihood-free bayesian estimation method for large-scale simulation models (in particular Agent-Based Models) that is effective even when the compute budget for simulating the model is limited.
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2020.02.01 Macroeconomic simulation comparison with a multivariate extension of the Markov information criterion
Journal of Economic Dynamics and Control
This paper develops a multivariate extension of the previous Markov Information Criterion, and shows that it can reliably be used for model comparison/selection in the context of macroeconomic simulation models.
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2016.11.01 Direct comparison of agent-based models of herding in financial markets
Journal of Economic Dynamics and Control
This paper provides an empirical application of the MIC methodology, comparing the performance of a set of popular agent-bsed models of financial volatility against a benchmark set of ARCH/GARCH processes.
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2016.10.03 A practical, accurate, information criterion for Nth order Markov processes
Computational Economics
This paper develops an information criterion for Markov processes which can be calculated using only sequences of simulated data from candidate models, enabling accurate comparison of performance across a wide range of model classes.
Skills
| Python | |
| General-purpose programming | |
| numpy/scipy computational applications | |
| torch/Pytorch for machine learning |
| Jupyter | |
| Interactive notebook widgets | |
| RISE interactive slideshows | |
| Data visualisation |
| C++ | |
| ABM simulation models | |
| Cython/Python integration |
| HTML | |
| Web development | |
| Jupyter/RISE integration |
| MATLAB | |
| Computational applications | |
| Teaching |
| Stata | |
| Econometric applications |
| QGIS | |
| GIS/spatial applications |
Languages
| French | |
| Native speaker |
| English | |
| Native speaker |
| Spanish | |
| Fluent |
Interests
| Economics | |
| Economic Geography | |
| Computational Economics | |
| Agent-based models |
| Econometrics | |
| Spatial Econometrics | |
| Bayesian Econometrics | |
| Likelihood-free inference |