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UID:20251020T040723EDT-9500vrxeNi@132.216.98.100
DTSTAMP:20251020T080723Z
DESCRIPTION:Chen Qui (LSE)\n	Date: February 5\, 2020\n	Time and Location: 10:
 00 a.m.\, Leacock 429\n\nAbstract:\n	This paper proposes a new minimax meth
 odology 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 e
 stimators\, called minimax learners\,and separately study their asymptotic
  properties as the ratio of controls to sample size goes to zero\, constan
 t 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 be-have stably\, maintain small mean square er
 ror and do not over control\; if selection bias is substantial\, minimax l
 earners are able to correctly reduce mean square error as more relevant co
 ntrols are added. When applied to the work of Ferraz and Finan (2011) on t
 he effects of electoral accountability on corruption\, minimax learners be
 have less erratically than OLS as well as other off-the-shelf shrinkage me
 thods and lead to more coherent conclusions\, even when the number of cont
 rols becomes very large\n
DTSTART:20200205T150000Z
DTEND:20200205T163000Z
LOCATION:Leacock 429\, Leacock Building\, CA\, QC\, Montreal\, H3A 2T7\, 85
 5 rue Sherbrooke Ouest
SUMMARY:Chen Qui (LSE)\, “Minimax Learning for Average Regression Functiona
 ls with an Application to Electoral Accountability and Corruption”
URL:/economics/channels/event/chen-qui-lse-minimax-lea
 rning-average-regression-functionals-application-electoral-accountability-
 308933
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