Summer School Brussels

So.Fi.E. Summer School and Conference on DSGE modelling

May 4-8 2026, Brussels (Belgium)

The SoFiE DSGE Course & Conference 2026 is a five-day event bringing together leading researchers in DSGE modeling and structural macroeconometrics. It features two components:

Training School (May 4-6): A three-day intensive training school in Bayesian Structural Macroeconometrics.
Instructors: Marco Del Negro (Federal Reserve Bank of New York) and Frank Schorfheide (University of Pennsylvania).
Research Conference (May 7-8): A two-day research conference on “The Past, the Present, and the Future of DSGE Models”, featuring presentations and discussions by leading scholars in macroeconomics.
Confirmed participants: Stéphane Adjemian, Guido Ascari, Marta Bańbura, Florin O. Bilbiie, Fabio Canova, Lawrence J. Christiano, Günter Coenen, Fiorella De Fiore, Ferre De Graeve, Marco Del Negro, Wouter den Haan, Hans Dewachter, Martin Eichenbaum, Stefano Eusepi, Francesco Furlanetto, Davide Furceri, Eric Ghysels, Domenico Giannone, Raffaella Giacomini, Eleonora Granziera, Romain Houssa, Joe Hazell, Leonardo Iania, Cosmin Ilut, Péter Karadi, Robert Kollmann, Michele Lenza, Jesper Lindé, Francesca Loria, Bartosz Maćkowiak, Leonardo Melosi, Karel Mertens, Emanuel Mönch, Francesca Monti, Roberto Motto, Rigas Oikonomou, Gert Peersman, Bruce Preston, Giorgio Primiceri, Andrea Raffo, Ricardo Reis, Werner Roeger, Lorenza Rossi, Massimo Rostagno, Juan Rubio-Ramírez, Yuliya Rychalovska, Chiara Scotti, Stéphanie Schmitt-Grohé, Frank Schorfheide, Sergey Slobodyan, Frank Smets, Andrea Tambalotti, Oreste Tristani, Harald Uhlig, and Sébastien Villemot.

The audience includes graduate students, academics, practitioners, and policymakers. Apply here.

Bayesian Structural Macroeconometrics

From DSGE to HANK Estimation, Policy Analysis, and Forecasting

So.Fi.E. Summer School

May 4-6 2026, Brussels (Belgium)

This three-day course, designed for Ph.D. students, researchers, and practitioners, teaches computational and econometric techniques that provide the building blocks for estimating dynamic stochastic general equilibrium (DSGE) models and their application in research, policy analysis, and forecasting. The course will cover both representative agent and heterogeneous agent New Keynesian (HANK) models, as well as macroeconometric approaches such as DSGE-VARs and functional VARs that can be used to evaluate the empirical plausibility and fit of these models. The course will strive to provide sufficient theory to understand the tools’ theoretical underpinnings, while emphasizing their practical applications in macro scenarios.

Instructors

Marco Del Negro

Marco Del Negro is an economic research advisor in Macroeconomic and Monetary Studies at the Federal Reserve Bank of New York. He is also the director of the NY Fed's Applied Macroeconomics and Econometrics Center (AMEC), a CEPR Research Fellow, and coeditor of the Journal of Applied Econometrics. Mr. Del Negro's research focuses on structural macroeconometrics, and in particular on the use of general equilibrium models in economic analysis, policy evaluation, and forecasting. Before joining the Bank, he was a research economist with the Macro group of the research department of the Federal Reserve Bank of Atlanta and an assistant professor at ITAM, Mexico City.

Frank Schorfheide

Frank Schorfheide is a Professor of Economics at the University of Pennsylvania, a Research Associate at the NBER, and a Research Fellow at the CEPR. He has served on the editorial board (as co-editor) of the International Economic Review (2005-2009) and as co-editor of Quantitative Economics (2011-2018). He is a Visiting Scholar at several central banks. Frank Schorfheide's research areas are econometrics and empirical macroeconomics. Much of his work can be classified as macroeconometrics and is related to the Bayesian analysis of dynamic stochastic general equilibrium (DSGE) models. His research provides a set of tools that are useful for empirical work with modern macroeconomic models, including forecasting and policy analysis. He has applied these methods to analyze the sources of business cycle fluctuations and to study the effects of monetary policy. In recent years, he has also worked on forecasting with dynamic panel data models and, more generally, on the estimation of models with unobserved heterogeneity.

Schedule
(to be updated soon)

Day 1 (Monday)

Lectures

09:00 – 10:15

1.1 Introduction to Bayesian Inference: from priors to posteriors; marginal likelihoods and model comparison.

10:45 – 12:00

1.2 DSGE Model Likelihood Function and Filtering: statespace representation of DSGE models (linear vs. nonlinear); using filters (Kalman filter and particle filter) to learn about latent states and to compute the likelihood function; likelihood without filtering.

13:30 – 14:45

1.3 Markov Chain Monte Carlo (MCMC) and Sequential Monte Carlo (SMC) for DSGE Models: Metropolis-Hastings algorithm; tracking convergence of posterior draws; transformation of draws for substantive work; SMC basic algorithm; data tempering for online estimation and forecasting; model tempering.

15:15 – 16:30

1.4 Introducing a Simple Workhorse DSGE Model: from model specification to equilibrium conditions; steady state; linearization and solution; estimation.

Participant Presentations.

16:45 – 17:45

Day 2 (Tuesday)

Lectures

09:00 – 10:15

2.1 Macroeconomic Analysis with DSGE Models: computation of impulse responses; variance and shock decompositions; extraction of latent variables (e.g., r-star); prior and posterior predictive checks; parameter identification.

10:45 – 12:00

2.2 Forecasting with DSGE Models: Review of Del Negro and Schorfheide handbook chapter and experience at the New York Fed.

13:30 – 14:45

2.3 Interplay of DSGE Models and VARs: DSGE-VARs; using DSGE models to identify structural shocks in VARs; using VARs to determine to what extent, and in which dimensions, a DSGE model is misspecified

15:15 – 16:30

2.4 Policy Analysis with Misspecified DSGE Models: using VARs (McKay and Wolf approach) and DSGE-VARs to analyze changes in policy rules.

Participant Presentations.

16:45 – 17:45

Day 3 (Wednesday)

Lectures

09:00 – 10:15

3.1 From representative to heterogeneous agent models: steady state, linearization, simulating macro and micro data

10:45 – 12:00

3.2 Adding micro-level estimation: panel versus functional approaches, functional VARs as benchmark, approximate filtering to evaluate functional VAR (and HA DSGE) likelihood function.

13:30 – 14:45

3.3 Estimation of a functional VAR based on simulated data from a heterogeneous agent model.

15:15 – 16:30

3.4 Outline of an estimation strategy for an HA model, a glance at recent papers that estimate heterogeneous agent models

Participant Presentations.

16:45 – 17:45

Key references

Del Negro, Marco, and Frank Schorfheide, Bayesian Macroeconometrics, Geweke, Koop, and van Dijk (eds.) The Oxford Handbook of Bayesian Econometrics, 2011, Oxford University Press, 293-389.
Del Negro, Marco, and Frank Schorfheide. ”DSGE model-based forecasting.” Handbook of economic forecasting. Vol. 2. Elsevier, 2013. 57-140.
Herbst, Edward and Frank Schorfheide (2015): Bayesian Estimation of DSGE Models, Princeton University Press, Princeton.
Fernandez-Villaverde, Jesus, Juan Rubio-Ramirez, and Frank Schorfheide (2016) “Solution and Estimation Methods for DSGE Models,” in: H. Uhlig and J. Taylor (eds.): Handbook of Macroeconomics, Vol 2., p.527-724, Elsevier, New York

DSGE MODELS

The present, the past and the future

Conference

May 7-8 2026, Brussels (Belgium)

The conference will be organized around thematic panels. Confirmed participants are:
Stéphane Adjemian, Guido Ascari, Marta Bańbura, Florin O. Bilbiie, Fabio Canova, Lawrence J. Christiano, Günter Coenen, Fiorella De Fiore, Ferre De Graeve, Marco Del Negro, Wouter den Haan, Hans Dewachter, Martin Eichenbaum, Stefano Eusepi, Francesco Furlanetto, Davide Furceri, Eric Ghysels, Domenico Giannone, Raffaella Giacomini, Eleonora Granziera, Romain Houssa, Joe Hazell, Leonardo Iania, Cosmin Ilut, Péter Karadi, Robert Kollmann, Michele Lenza, Jesper Lindé, Francesca Loria, Bartosz Maćkowiak, Leonardo Melosi, Karel Mertens, Emanuel Mönch, Francesca Monti, Roberto Motto, Rigas Oikonomou, Gert Peersman, Bruce Preston, Giorgio Primiceri, Andrea Raffo, Ricardo Reis, Werner Roeger, Lorenza Rossi, Massimo Rostagno, Juan Rubio-Ramírez, Yuliya Rychalovska, Chiara Scotti, Stéphanie Schmitt-Grohé, Frank Schorfheide, Sergey Slobodyan, Frank Smets, Andrea Tambalotti, Oreste Tristani, Harald Uhlig, and Sébastien Villemot.

How to participate?

To participate to the full event, submit your application by 1 April 2026 via email to Prof. Leonardo Iania (leonardo.iania@uclouvain.be), with “SoFiE 2026” in the subject line.

Applications must include a CV and a short motivation statement (in the email body). Applicants wanting to present a paper during the training school on "Bayesian Structural Macroeconometrics" should indicate this and submit a draft of the paper. Only selected applicants will be able to present.

Acceptance decisions will be emailed by 5 April 2026. For early applications, decisions may be communicated earlier to facilitate logistics.

Fees: €400 (PhD students), €800 (post-docs & faculty), €1200 (others)

Fees cover registration, lunches, and coffee breaks. All accepted participants must be SoFiE members (or join it before the event). The event is free for full-time Belgian university students/researchers (application is compulsory). Membership info here.