BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//CREST - ECPv4.9.9//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/Helsinki
BEGIN:DAYLIGHT
TZOFFSETFROM:+0200
TZOFFSETTO:+0300
TZNAME:EEST
DTSTART:20190331T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0300
TZOFFSETTO:+0200
TZNAME:EET
DTSTART:20191027T010000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Europe/Helsinki:20191010T121500
DTEND;TZID=Europe/Helsinki:20191010T131500
DTSTAMP:20191018T120843
CREATED:20191003T090207Z
LAST-MODIFIED:20191003T090207Z
UID:10114-1570709700-1570713300@crest.science
SUMMARY:Yannick Guyonvarch (CREST): "Finite sample inference in linear regression without Normal errors"
DESCRIPTION:Firms and Markets Seminar \n \nAbstract: \nWe propose confidence intervals for the coefficients in a linear regression model that are valid with i.i.d observations for every sample size larger than two. We do not resort to the assumption that the errors are normally distributed to get our result. Our construction only requires moment restrictions on the data generating process (DGP) to allow for the use of Berry-Esseen-type arguments as well as so-called concentration inequalities for random variables and random matrices. We exhibit a lower bound on the confidence level they achieve in finite samples and we show that they are asymptotically of the same length as the confidence intervals based on asymptotic normality. We also discuss the issue of uniformity over classes of DGPs and how to improve our results under more stringent moment conditions. This work builds upon a very large literature that was crucially influenced by Berry (1941) and Esseen (1942).\n \nPlease note that this is an early-stage project.\n
URL:http://crest.science/event/yannick-guyonvarch-crest-finite-sample-inference-in-linear-regression-without-normal-errors
LOCATION:3001
CATEGORIES:The PhD Economics Seminars
END:VEVENT
END:VCALENDAR