Elysée Aristide Houndetoungan

Assistant Professor of Economics


Accepted and Published papers

  • Dufays, A., Houndetoungan, E. A., & Coën, A. (2022). Selective linear segmentation for detecting relevant parameter changes. Journal of Financial Econometrics, 20(4), 762-805. [links: paper, WP version]

Working Papers

  • Count Data Models with Social Interactions under Rational Expectations, 2022 - [links: paper, online supplement, R package]
  • I propose a peer effect model for counting variables using a game of incomplete information. I present sufficient conditions under which the game equilibrium is unique and study the model parameter identification. My identification result is general and can be applied to other nonlinear models with social interactions, such as binary and ordered response models. I show that the parameters can be estimated using the Nested Partial Likelihood (NLP). I generalize the estimator to the case of endogenous networks and study its asymptotic properties. I show that the linear-in-means/Tobit models with a counting outcome are particular cases of my model. However, these models ignore the counting nature of the dependent variable and lead to inconsistent estimators. I use the model to evaluate peer effects on students' participation in extracurricular activities. I find that increasing the expected number of activities in which a student's friends are enrolled by one implies an increase in the expected number of activities in which the student is enrolled by 0.08. The estimate of this effect with the Tobit model is three times higher.

  • Estimating Peer Effects using Partial Network Data, 2022 (with Vincent Boucher) - [links: paper, R package] [vignette]

    We study the estimation of peer effects through social networks when researchers do not observe the entire network structure. Special cases include sampled networks, censored networks, misclassified links, and aggregated relational data. We assume that researchers can obtain a consistent estimator of the distribution of the network. We show that this assumption is sufficient for estimating peer effects using a linear-in-means model. We provide an empirical application to the study of peer effects on students academic achievement using the widely used Add Health database and show that network data errors have a first-order downward bias on estimated peer effects.

Work In Progress

  • Hospital Length of Stays, Readmissions and Deaths: A Multiple Spell Survival Analysis (whith Damien Echevin and Bernard Fortin)
  • Physicians' Financial Incentives (whith Damien Echevin)
  • Quasi-Maximum Likelihood Estimator for Peer Effect Models with Partial Network Data
  • Asymptotic Efficiency for Two-Stage Conditional M-estimators (with Abdoul Haki Maoude)
  • Identifying peer effects on academic achievements through students' effort (with Cristelle Kouame)