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
• E-mail: lucas.girard[at]ensae.fr
• Personal Website: https://faculty.crest.fr/lgirard/

Curriculum Vitae

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 “small-unit 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:

1. “segregsmall: A command to estimate segregation in the presence of small units” with Xavier D’Haultfoeuille and Roland Rathelot, Stata Journal, 21(1), pp. 152-179, March 2021

2. 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, socio-economic status)
3. 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 (1873-2016).

My second research theme deals with constructing non-asymptotic 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 t-statistic. 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 non-asymptotic CIs. With Alexis Derumigny and Yannick Guyonvarch, we have several projects related to this theme, notably:

1. “Explicit non-asymptotic bounds for the distance to the first-order Edgeworth expansion,” arXiv:2101.05780
2. 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.