BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//CREST - ECPv4.9.14//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:CREST
X-ORIGINAL-URL:http://crest.science
X-WR-CALDESC:Events for CREST
BEGIN:VTIMEZONE
TZID:Europe/Paris
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20200329T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20201025T010000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Europe/Paris:20200115T113000
DTEND;TZID=Europe/Paris:20200115T124500
DTSTAMP:20200126T083730
CREATED:20200107T145405Z
LAST-MODIFIED:20200108T091245Z
UID:10263-1579087800-1579092300@crest.science
SUMMARY:QIU Chen (LSE) "Minimax Learning for Average Regression Functionals with an Application to Electoral Accountability and Corruption"
DESCRIPTION:Time: 12:30pm – 13:45 pm\nDate:15th of January 2020\nPlace: Room 3001 \nQIU Chen (LSE) “Minimax Learning for Average Regression Functionals with an Application to Electoral Accountability and Corruption” \nAbstract : This paper proposes a new minimax methodology to estimate average regression functionals\, which are relevant to many empirical problems including average treatment effects. Embedded in a penalized series space\, this strategy exploits a minimax property of a key nonparametric component of the average regression functional and aims to directly control main remainder bias. I then construct a new class of estimators\, called minimax learners\, and separately study their asymptotic properties as the ratio of controls to sample size goes to zero\, constant and infinity. Root-n normality is established under weak conditions for all three cases. Minimax learners are straightforward to implement due to their minimum distance representation. In simulations where selection bias is mild\, minimax learners behave stably\, maintain small mean square error and do not over control; if selection bias is substantial\, minimax learners are able to correctly reduce mean square error as more relevant controls are added. When applied to the work of Ferraz and Finan (2011) on the effects of electoral accountability on corruption\, minimax learners behave less erratically than OLS as well as other off-the-shelf shrinkage methods and lead to more coherent conclusions\, even when the number of controls becomes very large. \n
URL:http://crest.science/event/qiu-chen-lse-minimax-learning-for-average-regression-functionals-with-an-application-to-electoral-accountability-and-corruption
CATEGORIES:Economics
END:VEVENT
END:VCALENDAR