Calendrier du 09 janvier 2023
GSIELM (Graduate Students International Economics and Labor Market) Lunch Seminar
Du 09/01/2023 de 13:00 à 14:00
Maison des Sciences Economiques, Salle 116
DENAGISCARDE Olivier (CES/BNP Paribas Real Estate)
Working From Home and Business Districts: Evidence From the Paris Metropolitan Area
In this preliminary work, I provide a first empirical analysis of the intracity effect of Working From Home (WFH) on office property markets at the municipality level in the Paris metropolitan area, by taking advantage of the Covid-19 crisis as a natural experiment. In addition, I similarly explore the impact on local consumer services as another dimension of the consequences of the rise of WFH.
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Job Market Seminar
Du 09/01/2023 de 12:00 à 13:15
R2-01
MUGNIER Martin (CREST, ENSAE, Institut Polytechnique de Paris)
Unobserved Clusters of Time-Varying Heterogeneity in Nonlinear Panel Data Models
In studies based on longitudinal data, researchers often assume time-invariant unobserved
heterogeneity or linear-in-parameters conditional expectations. Violation of these assumptions
may lead to poor counterfactuals. I study the identification and estimation of a large class
of nonlinear grouped fixed effects (NGFE) models where the relationship between observed
covariates and cross-sectional unobserved heterogeneity is left unrestricted but the latter only
takes a restricted number of paths over time. I show that the corresponding “clusters” and the
nonparametrically specified link function can be point-identified when both dimensions of the
panel are large. I propose a semiparametric NGFE estimator and establish its large sample
properties in popular binary and count outcome models. Distinctive features of the NGFE
estimator are that it is asymptotically normal unbiased at parametric rates, and it allows for
the number of periods to grow slowly with the number of cross-sectional units. Monte Carlo
simulations suggest good finite sample performance. I apply this new method to revisit the so-
called inverted-U relationship between product market competition and innovation. Allowing
for clustered patterns of time-varying unobserved heterogeneity leads to a less pronounced
inverted-U relationship.