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PRODID:-//AMSE//Event Calendar//FR
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UID:event-12161@amse-aixmarseille.fr
DTSTAMP:20260430T131052Z
CREATED:20260430T131052Z
LAST-MODIFIED:20260430T131052Z
STATUS:CONFIRMED
SEQUENCE:0
SUMMARY:big data and econometrics seminar - Koen Jochmans
DTSTART:20250506T120000Z
DTEND:20250506T133000Z
DESCRIPTION:A popular approach to perform inference on a target parameter i
 n the presence of nuisance parameters is to construct estimating equations 
 that are orthogonal to the nuisance parameters\, in the sense that their ex
 pected first derivative is zero. Such first-order orthogonalization may\, h
 owever\, not suffice when the nuisance parameters are very imprecisely esti
 mated. Leading examples where this is the case are models for panel and net
 work data that feature fixed effects. In this paper\, we show how\, in the 
 conditional-likelihood setting\, estimating equations can be constructed th
 at are orthogonal to any chosen order. Combining these equations with sampl
 e splitting yields higher-order bias-corrected estimators of target paramet
 ers. In an empirical application we apply our method to a fixed-effect mode
 l of team production and obtain estimates of complementarity in production 
 and impacts of counterfactual re-allocations.\\n\\nContact: Sullivan Hué: 
 sullivan.hue[at]univ-amu.frMichel Lubrano: michel.lubrano[at]univ-amu.fr\n\
 nPlus d'informations: https://amse-aixmarseille.fr/en/events/koen-jochmans-
 
LOCATION:Îlot Bernard du Bois - Salle 21\, AMU - AMSE\, 5-9 boulevard Maur
 ice Bourdet\, 13001 Marseille
URL;VALUE=URI:https://amse-aixmarseille.fr/en/events/koen-jochmans-0
CONTACT:Sullivan Hué: sullivan.hue[at]univ-amu.frMichel Lubrano: michel.lu
 brano[at]univ-amu.fr
TRANSP:OPAQUE
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