BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//CREST - ECPv5.1.3//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:CREST
X-ORIGINAL-URL:https://crest.science
X-WR-CALDESC:Events for CREST
BEGIN:VTIMEZONE
TZID:Europe/Helsinki
BEGIN:DAYLIGHT
TZOFFSETFROM:+0200
TZOFFSETTO:+0300
TZNAME:EEST
DTSTART:20260329T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0300
TZOFFSETTO:+0200
TZNAME:EET
DTSTART:20261025T010000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Europe/Helsinki:20260518T160000
DTEND;TZID=Europe/Helsinki:20260518T171500
DTSTAMP:20260709T184222
CREATED:20260409T113915Z
LAST-MODIFIED:20260409T114139Z
UID:18906-1779120000-1779124500@crest.science
SUMMARY:Karun ADUSUMILLI (University of Pennsylvania) "You've Got to be Efficient: Ambiguity\, Misspecification and Variational Preferences"
DESCRIPTION:PSE Seminar : \nTime: 16:00 pm – 17:15 pm\nDate: 18th of may\nRoom : 3001 \n  \nKarun ADUSUMILLI (University of Pennsylvania) –  “You’ve Got to be Efficient: Ambiguity\, Misspecification and Variational Preferences” \n  \nAbstract : \nThis article introduces a framework for evaluating statistical decisions under both prior ambiguity and likelihood misspecification. We begin with an ambiguity set — a frequentist model that pairs a possibly misspecified likelihood with every possible prior — and uniformly expand it by a Kullback–Leibler radius to accommodate likelihood misspecification. We show that optimal decisions under this framework are equivalent to minimax decisions with an exponentially tilted loss function. Misspecification manifests as an exponential tilting of the loss\, while ambiguity corresponds to a search for the least favorable prior. This separation between ambiguity and misspecification enables local asymptotic analysis under global misspecification\, achieved by localizing the priors alone. Remarkably\, for both estimation and treatment assignment\, we show that optimal decisions coincide with those under correct specification\, regardless of the degree of misspecification. These results extend to semi-parametric models. As a practical consequence\, our findings imply that practitioners should prefer maximum likelihood over the simulated method of moments\, and efficient GMM estimators — such as two-step GMM — over diagonally weighted alternatives. \n  \nOrganizer :\nLaurent DAVEZIES (Pôle économie du CREST) \nSponsors:\nCREST \n
URL:https://crest.science/event/https-sites-google-com-site-adusumik/
CATEGORIES:Paris Econometrics Seminar,Seminars
ATTACH;FMTTYPE=:
END:VEVENT
END:VCALENDAR