Introduction to Topological Data Analysis – Bertrand Michel (Ecole Centrale de Nantes)








4th June 2024


From 2pm to 5pm






5th June 2024


From 2pm to 5pm




With the recent explosion in the amount, the variety and the dimensionality of available data, identifying, extracting and exploiting their underlying structure has become a problem of fundamental importance for data analysis and statistical learning. Topological Data Analysis (TDA) is a recent field whose aim is to uncover, understand and exploit the topological and geometric structure underlying complex and possibly high dimensional data. It proposes new well-founded mathematical theories and computational tools that can be used independently or in combination with other data analysis and statistical learning techniques.


The course will be organized around the following topics that play a central role in TDA:

  1. Persistent homology: an introduction (simplicial complexes, filtrations, homology,…).
  2. Mapper, a topological tool for data exploration and visualization.
  3. Applications of persistent homology in TDA: clustering, topological signatures, statistical aspects, TDA + Deep Learning

No specific background in topology is required to follow the course. Practical sessions (with Python, Jupyter Lab and Gudhi library) will be organized to illustrate the concepts and help the audience to become familiar with the tools of TDA.

Structural Econometrics – methods and applications

Structural Econometrics – methods and applications

Hélène Turon
University of Bristol


SCHEDULE Tuesday 28th April 2020 De 09h00 à 12h00 Salle 2009
Monday 04th May 2020 De 09h00 à 11h00 Salle 2009
Thursday 30th April 2020
07th May2020
De 09h00 à 12h00
De 09h00 à 11h00
Salle 2009


The aim of this course is to provide an accessible introduction to structural estimation methods, with some illustrative examples and hands-on exercises. These methods have been used in labour economics but also in many other fields such as industrial organization, health economics, development economics. They allow researchers to confront rich theoretical models to the data in order to test their credibility and to carry out simulations of counterfactual policies. Modelling and estimation methods often need to be designed jointly to afford both identification and computational feasibility. We will aim to understand, with the aid of examples from the literature, what key features of the data, and what theoretical assumptions drive the values of parameter estimates and whether the conclusions drawn from the estimation are robust to alternative assumptions. We will also compare the relative appeals of structural estimation and reduced form estimation with natural experiments.


1. Methods 1: Method of (Simulated) Moments, Method of Simulated Likelihood.
2. Methods 2: Indirect Inference, Identification. Comparison with natural experiments.
3. Applications 1: Hands-on structural estimation of simple examples.
4. Applications 2: Three examples in labour research: Keane and Wolpin (2001), Rust (1987), Meghir, Narita, Robin (2015).


Adda, J., Cooper, R., & Cooper, R. W. (2003). Dynamic economics: quantitative methods and applications. MIT press.
Arcidiacono, P., & Jones, J. B. (2003). Finite mixture distributions, sequential likelihood and the EM algorithm. Econometrica, 71(3), 933-946.
Davidson, R., & MacKinnon, J. G. (2004). Econometric theory and methods (Vol. 5). New York: Oxford University Press.
French, E., & Taber, C. (2011). Identification of models of the labor market. In Handbook of Labor Economics (Vol. 4, pp. 537-617). Elsevier.
Keane, M. P. (2010). Structural vs. atheoretic approaches to econometrics. Journal of Econometrics, 156(1), 3-20.
Keane, M. P., Todd, P. E., & Wolpin, K. I. (2011). The structural estimation of behavioral models: Discrete choice dynamic programming methods and applications. In Handbook of labor economics (Vol. 4, pp. 331-461). Elsevier.
Keane, M. P., & Wolpin, K. I. (2001). The effect of parental transfers and borrowing constraints on educational attainment. International Economic Review, 42(4), 1051-1103.
McFadden, D. (1989). A method of simulated moments for estimation of discrete response models without numerical integration. Econometrica, 995-1026.
Meghir, C., Narita, R., & Robin, J. M. (2015). Wages and informality in developing countries. American Economic Review, 105(4), 1509-46.
Rust, J. (1987). Optimal replacement of GMC bus engines: An empirical model of Harold Zurcher. Econometrica, 999-1033.
Rust, J. (1996). Numerical dynamic programming in economics. Handbook of computational economics, 1, 619-729.