12 & 13/11/2026 - Foyer Royal - Aula Magna

Please register via this form :Identity and Policy Design workshop 

Economic decisions are often shaped by social identity, through norms, group membership, or self-image. The study of its implications for behavior is expanding to an increasingly broad set of groups and norms (e.g., gender, race, class, age, caste), and its normative implications are receiving growing attention. In this workshop, we aim to bring together researchers working on theory and empirics, on positive and normative questions alike, to inform better policy design.

Program 

29/10/2026 - 12:45 - 14:00 - DOYEN 22

Philippe Goulet Coulombe 

(Université du Québec)

will give a presentation on 

Implied Factors: The Linear Skeleton of Machine Learning Forecasts

Abstract: 

For interpretability, sparsity is key and linearity is preferable. Yet neither is common in machine-learning-based macroeconomic forecasting, where large predictor sets are fed into nonlinear, nonparametric models. We show that any supervised learning algorithm whose predictions are linear in the target variable—regardless of nonlinearities in the features—admits a linear-factor representation. We call the resulting components the model’s implied factors. Studying these factors provides a new route to uncover low-dimensional structure, even when the model was not built with such structure in mind. Empirically, two or three implied factors suffice to reproduce out-of-sample forecasts of U.S. GDP, inflation, and unemployment. Across models, the leading direction is common and these factors cluster into a few common signals, while higher-order factors differ by family, so cross-model differences are quantitative rather than conceptual. Used as conditioning controls in local projections, implied factors from a random forest deliver correctly signed inflation responses where standard PCA controls leave the price puzzle intact.

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20/10/2026 - LECL 61 - 12:45

Gonzague Vannoorenberghe

(UCLouvain, LIDAM, IRES)

will give a presentation on 

Trade diversification, volatility and welfare

Abstract: 

Policy discussions of import dependence typically rely on heuristic approaches featuring Herfindahl indices of import shares. We provide a formal derivation of the welfare cost of trade shocks for products with CES demand, risk-averse consumers and convex short-run supply curves. In this setup, the trade elasticity combines the demand-side substitution elasticity with the curvature of the supply curve, and expected welfare losses depend on a trade-elasticity-augmented Herfindahl index. We show that the diversification of suppliers can have very different consequences depending on whether varieties are complements or substitutes, and on the shape of the supply curve, which makes shocks more akin to price or quantity shocks. When supplier wages respond to shocks, a second margin appears: the concentration of suppliers' destinations and the importer's own share of world consumption matter alongside its import concentration. We illustrate the impact of our augmented measure of trade dependence with product-level trade data for the European Union.

(joint with Andrea Galea)

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23/10/2026 - De 10h30 à 12h30 - Foyer Royal ( Aula Magna), Louvain-la-Neuve
 

Le rapport du CEV

Orateurs :   

• Baudouin Regout  (Federaal Planbureau)
• Jean Hindriks (UCLouvain/CORE)

Formulaire d'inscription : Pension mornings UCLouvain/Ethias 2026.

30/10/2026 - 11:00 - LIDAM D.251

Massimiliano Marcellino

(Bocconi University) 

will give a presentation on 

Matrix Three-Pass Regression Filter: A Nowcasting Framework for Mixed-Frequency Multidimensional Datats

Abstract: 

We contribute to the literature on large multidimensional data by proposing a Matrix Mixed-Frequency Three-Pass Regression Filter (Matrix MF–TPRF) for nowcasting with high-dimensional, ragged-edge information sets. The method extends proxy-based factor extraction to matrix-valued predictors, preserving cross-sectional structure while accommodating mixed frequencies and missing observations. After discussing the procedure and its properties, Monte Carlo evidence shows that exploiting the matrix structure improves predictive-signal recovery and nowcasting performance relative to vector approaches, especially with persistent non-predictive components. A pseudo real-time application to GDP growth in eight major euro-area countries indicates sizeable gains at early vintages and in the post-Covid period, when cross-country co-movement is stronger. The results also highlight the importance of parsimonious information sets for stable real-time performance.

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21/10/2026 - 12h50 - LIDAM D.251

Martial Toniotti

will present

2010s: Wednesday, 21 October 2026

Anthony Papavasiliou, Yves Smeers
Remuneration of flexibility using operating reserve demand curves: A case study of Belgium
In : The Energy Journal, 2017, vol. 38(6), p. 105-135