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BEGIN:VEVENT
UID:2aa99d8fb4e6803f4a21d5b9006a2976
CATEGORIES:Seminars
CREATED:20190228T101633
SUMMARY:Lunch Seminar: Stéphane Bonhomme - University of Chicago
DESCRIPTION;ENCODING=QUOTED-PRINTABLE:\n\nPosterior Average Effects (joint with Martin Weidner)\n\n\nAbstract:\nT
 he applied economist is often interested in computing an average with respe
 ct to a distribution of unobservables. Common examples are average partial 
 effects in discrete choice, moments of individual fixed-effects in panel da
 ta, or counterfactual policy simulations based on a structural model. We co
 nsider empirical Bayes (EB) estimators of such effects, where the average i
 s computed conditional on the observation sample. While EB estimators are s
 ometimes used to “shrink” individual estimates – e.g., of teacher value-add
 ed or hospital quality – toward a common mean and reduce estimation noise, 
 a study of their frequentist properties is still lacking. We establish two 
 robustness properties of EB estimators under misspecification of the assume
 d distribution of unobservables: EB estimators are optimal in terms of loca
 l (worst-case) bias, and their global bias is no larger than twice the mini
 mum bias that can be achieved within a large class of estimators of average
  effects. These results provide a rationale for the use of empirical Bayes 
 estimators, beyond the settings to which they have been applied so far.\n
DTSTAMP:20260405T192610Z
DTSTART:20190305T133000Z
DTEND:20190305T143000Z
SEQUENCE:0
TRANSP:OPAQUE
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