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:20260316T121500
DTEND;TZID=Europe/Helsinki:20260316T133000
DTSTAMP:20260709T203954
CREATED:20260302T133156Z
LAST-MODIFIED:20260305T104626Z
UID:18847-1773663300-1773667800@crest.science
SUMMARY:Domenico GIANNONE (Johns Hopkins University) "Bayesian Inference in IV Regression"
DESCRIPTION:[vc_row][vc_column][vc_column_text]Macro seminar\nTime : 12h15 – 13h30 \nDate : 16th  March 2026 \nSalle 3001 \nDomenico GIANNONE (Johns Hopkins University) “Bayesian Inference in IV Regression” \nAbstract: It is well known that standard frequentist inference breaks down in IV regressions with weak instruments. Bayesian inference with diffuse priors suffers from the same problem. We show that the issue arises because flat priors on the first-stage coefficients overstate instrument strength. In contrast\, inference improves drastically when an uninformative prior is specified directly on the concentration parameter—the key nuisance parameter capturing instrument relevance. The  resulting Bayesian credible intervals are asymptotically equivalent to the frequentist confidence intervals based on conditioning approaches\, and remain robust to weak instruments.\n \nJoint work : Michele Lenza and Giorgio Primiceri \nOrganizer :  Alessandro RIBONI \n
URL:https://crest.science/event/domenico-giannone-johns-hopkins-university-bayesian-inference-in-iv-regression/
CATEGORIES:Macroeconomics,Seminars
ATTACH;FMTTYPE=:
END:VEVENT
END:VCALENDAR