Christophe Gaillac - Nuffield College Oxford University

People Feature

Christophe Gaillac

Postdoctoral Prize Research Fellow in Economics

My research focuses on Econometrics, Statistics, and Machine Learning.

I have also interests in Labor Economics and Political Science.

I hold a PhD in Economics from the Toulouse School of Economics. I am also a research fellow at the Center for Research in Economics and Statistics (CREST). I was coordinator of statistical and mathematical teachings at ENSAE in 2017-2019. You can find my CV here.

You can find here the handout of the course Machine learning for Econometrics.

T: +33669049330
E: christophe.gaillac@nuffield.ox.ac.uk
Nuffield College
New Road
Oxford
OX1 1NF
My personal website: https://www.cgaillac.com/

Publications

Rationalizing Rational Expectations? Characterization and Tests, with Xavier D’Haultfoeuille (CREST) and Arnaud Maurel (Duke university), Quantitative Economics, 12 (3): 817-842 (2021).

Adaptive estimation in the linear random coefficients model when regressors have limited variation, with Eric Gautier (TSE), Forthcoming, Bernoulli.
Estimates for the SVD of the truncated Fourier transform on L2(exp(b|×|)) and stable analytic continuation, with Eric Gautier (TSE), Journal of Fourier Analysis and Applications (2021) 27:72.

Working papers:

Designing Labor Market Recommender Systems: the Importance of Job Seeker Preferences and Competition, with Victor Alfonso Naya (LISN), Guillaume Bied (CREST-LISN), Philippe Caillou (LISN), Bruno Crépon (CREST), Elia Perennes (CREST) and Michele Sebag (LISN).
Non Parametric Classes for Identification in Random Coefficients Models when Regressors have Limited Variation, with Eric Gautier (TSE).

R & Stata Packages:

RationalExp: test of the rational expectations hypothesis based on the marginal distributions of realizations and subjective beliefs.
RandomCoefficients: implements the estimator proposed in Gaillac and Gautier (2019).
R package MarginalFElogit and Stata program mfelogit: implement the estimators of the sharp bounds on the AME and the related confidence intervals on the AME and ATE from Davezies, D’Haultfoeuille, and Laage, 2021.