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:20260116T121500
DTEND;TZID=Europe/Helsinki:20260116T133000
DTSTAMP:20260709T224924
CREATED:20251229T081516Z
LAST-MODIFIED:20260106T091014Z
UID:18674-1768565700-1768570200@crest.science
SUMMARY:Yiqi LIU  (Cornell University ) "Synthetic Parallel Trends"
DESCRIPTION:[vc_row][vc_column][vc_column_text]Macro seminar\nTime : 12h15 – 13h30 \nDate : 16th  January 2026 \nSalle 3001 \nYiqi LIU (Cornell University ) “Synthetic Parallel Trends” \nAbstract: Popular empirical strategies for policy evaluation in the panel data literature—including difference-in-differences (DID)\, synthetic control (SC) methods\, and their variants—rely on key identifying assumptions that can be expressed through a specific choice of weights relating pre-treatment trends to the counterfactual outcome. While each choice of weights may be defensible in empirical contexts that motivate a particular method\, it relies on fundamentally untestable and often fragile assumptions. I develop an identification framework that allows for all weights satisfying a Synthetic Parallel Trends assumption: the treated unit’s trend is parallel to a weighted combination of control units’ trends for a general class of weights. The framework nests these existing methods as special cases and is by construction robust to violations of their respective assumptions. I construct a valid confidence set for the identified set of the treatment effect\, which admits a linear programming representation with estimated coefficients and nuisance parameters that are profiled out. In simulations where the assumptions underlying DID or SC-based methods are violated\, the proposed confidence set remains robust and attains nominal coverage\, while existing methods suffer severe undercoverage.  \n  \n  \n
URL:https://crest.science/event/yiqi-liu-cornell-university-t-b-a/
CATEGORIES:Macroeconomics,Seminars
ATTACH;FMTTYPE=:
END:VEVENT
END:VCALENDAR