Lucas Girard
Ph.D. Candidate in Economics
 Address: 5 avenue Henry Le Chatelier, Palaiseau, 91120, France
 Office: 3104
 Phone: +33 (0)1 70 26 69 57
 Email: lucas.girard[at]ensae.fr
 Personal Website: https://faculty.crest.fr/lgirard/
Biography
I am a Ph.D. candidate at CREST (Center for Research in Economics and Statistics) under the supervision of Xavier D’Haultfoeuille and a Teaching Fellow at ENSAE Paris.
I am interested in econometric theory and its application. My current research is devoted to two main themes.
First, I wonder how to construct reliable measures of polarization/segregation, namely indices that quantify to which extent two groups tend to make different choices among a set of options. This problem encompasses various settings: residential or school segregation between, for instance, natives and immigrants, speech polarization between Democrats and Republicans, buyers’ choices for different goods, etc. I investigate situations of “high dimensional choices”: when the number of observed choices in the data is low relative to the total number of distinct options. In such cases, natural measures of polarization suffer from a “smallunit bias.” Consequently, other methods need to be developed to yield measures that can reliably be compared over time or across different settings. I have three projects related to this theme:

“segregsmall: A command to estimate segregation in the presence of small units” with Xavier D’Haultfoeuille and Roland Rathelot, Stata Journal, 21(1), pp. 152179, March 2021

Ongoing work to measure residential segregation in France using Labor Force Survey data from 1968 to 2019 and compare it across various dimensions (nationality, ethnicity, socioeconomic status)
 An ongoing project joint with Xavier D’Haultfoeuille and Roland Rathelot proposes an alternative approach to Gentzkow, Shapiro, and Taddy (Econometrica 2019) to study speech polarization in the US Congress between Republican and Democrat speakers (18732016).
My second research theme deals with constructing nonasymptotic confidence intervals (CIs), i.e., whose probability of containing the target parameter is larger or equal to their nominal level for any sample size. It happens that the prevalent way to conduct inference in applied economics stems from asymptotic results. As a case in point, the CIs for individual coefficients in linear regressions rely on the asymptotic normality of the tstatistic. Their properties are, therefore, only asymptotic. The validity of inference for any real finite sample involves an asymptotic approximation, which might be questioned in some settings. Hence the potential interest of nonasymptotic CIs. With Alexis Derumigny and Yannick Guyonvarch, we have several projects related to this theme, notably:
 “Explicit nonasymptotic bounds for the distance to the firstorder Edgeworth expansion,” arXiv:2101.05780
 An ongoing project where we apply those bounds to construct nonasymptotic CIs for individual coefficients of linear regression models.
Finally, I like teaching, and I am a Teaching Fellow (Chargé d’enseignement / ATER) at ENSAE Paris, where I teach econometrics, economics, statistics, and mathematics courses.
Research Interests

Econometrics Econometric Theory

Economics Applied Microeconometrics

Statistics Natural Language Processing