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UID:event-13100@amse-aixmarseille.fr
DTSTAMP:20260413T215739Z
CREATED:20260413T215739Z
LAST-MODIFIED:20260413T215739Z
STATUS:CONFIRMED
SEQUENCE:0
SUMMARY:big data and econometrics seminar - Prosper Dovonon
DTSTART:20260428T120000Z
DTEND:20260428T133000Z
DESCRIPTION:This paper introduces a semiparametric Bayesian method for high
 -dimensional linear instrumental variables (IV) models. More specifically\,
  we develop a quasi-Bayesian framework for variable selection in high-dimen
 sional settings with endogenous regressors\, where instrumental variables a
 re available to address endogeneity. We study the properties of the quasi-p
 osterior distribution as the number of regressors increases and provide a s
 et of conditions under which the quasi-posterior concentrates asymptoticall
 y around the true parameter value. We also propose an efficient and easy-to
 -implement Markov chain Monte Carlo (MCMC) algorithm for sampling from the 
 quasi-posterior distribution. The finite-sample performance of the proposed
  method is evaluated through Monte Carlo experiments.\\n\\nContact: Sulliva
 n Hué : sullivan.hue[at]univ-amu.frMichel Lubrano : michel.lubrano[at]univ
 -amu.fr\n\nPlus d'informations: https://amse-aixmarseille.fr/fr/evenements/
 prosper-dovonon
LOCATION:Îlot Bernard du Bois - Salle 21\, AMU - AMSE\, 5-9 boulevard Maur
 ice Bourdet\, 13001 Marseille
URL;VALUE=URI:https://amse-aixmarseille.fr/fr/evenements/prosper-dovonon
CONTACT:Sullivan Hué : sullivan.hue[at]univ-amu.frMichel Lubrano : michel.
 lubrano[at]univ-amu.fr
TRANSP:OPAQUE
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