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:20210328T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0300
TZOFFSETTO:+0200
TZNAME:EET
DTSTART:20211031T010000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Europe/Helsinki:20210913T121500
DTEND;TZID=Europe/Helsinki:20210913T133000
DTSTAMP:20260721T201602
CREATED:20210726T081639Z
LAST-MODIFIED:20210903T050219Z
UID:12917-1631535300-1631539800@crest.science
SUMMARY:Aurélien EYQUEM  (University of Lyon 2)\,  "A Model-based Assessment of Inequalities in France"
DESCRIPTION:The Macroeconomics Seminar: \nTime: 12:15 pm – 13:30 pm\nDate: 13th of September  2021 \nAurélien EYQUEM (University of Lyon 2)\, “A Model-based Assessment of Inequalities in France” \nAbstract : We incorporate various microeconomic and macroeconomic evidence about individual income\, macroeconomic aggregates\, taxes and transfers in France stemming from distributional accounts starting in 1984 into a general equilibrium model with heterogeneous agents to quantify the contribution of various factors (the social and fiscal systems\, mark-ups\, aggregate productivity\, capital gains) to the evolution of pre- and post-tax income as well as wealth inequalities over time. The model fits data particularly well and allows for counterfactual experiments. These suggest that the social and fiscal systems are the most important drivers of income and wealth inequalities over the period. \nCo-écrit avec Stéphane Auray\, Bertrand Garbinti et Jonathan Goupille-Lebret. \nOrganizers:\n\nOlivier LOISEL (CREST) \nSponsors:\nCREST \n
URL:https://crest.science/event/aurelien-eyquem-university-of-lyon-2-t-b-a/
CATEGORIES:Macroeconomics
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Helsinki:20210913T140000
DTEND;TZID=Europe/Helsinki:20210913T151500
DTSTAMP:20260721T201602
CREATED:20210817T111444Z
LAST-MODIFIED:20210917T102249Z
UID:12940-1631541600-1631546100@crest.science
SUMMARY:Cheng Mao (Georgia Tech) - "Optimal Sparse Recovery of a Planted Vector in a Subspace"
DESCRIPTION:Statistical Seminar: Every Monday at 2:00 pm.\nTime: 2:00 pm – 3:15 pm\nDate: 13th of September 2021\nPlace: en visio \nCheng Mao (Georgia Tech)  – “Optimal Sparse Recovery of a Planted Vector in a Subspace” \nAbstract: We consider the task of recovering a pN-sparse vector planted in an n-dimensional random subspace of R^N\, given an arbitrary basis for the subspace. We give an improved analysis of (a slight variant of) a spectral method proposed by Hopkins\, Schramm\, Shi\, and Steurer (STOC 2016)\, showing that it succeeds when np << sqrt(N). This condition improves upon the best known guarantees of any polynomial-time algorithm. Our analysis also applies to the dense case p=1\, provided that the planted vector has entries sufficiently different from Gaussian. Furthermore\, we give a matching lower bound\, showing that when np >> sqrt(N)\, a general class of spectral methods fail to detect the planted vector. This yields a tight characterization of the power of this class of spectral methods and may suggest that no polynomial-time algorithm can succeed when np >> sqrt(N). \nOrganizers:\nCristina BUTUCEA (CREST)\, Alexandre TSYBAKOV (CREST)\, Karim LOUNICI (CMAP) \, Jaouad MOURTADA (CREST)\nSponsors:\nCREST-CMAP \n\n
URL:https://crest.science/event/cheng-mao-georgia-tech-tba/
CATEGORIES:Statistics
ATTACH;FMTTYPE=:
END:VEVENT
END:VCALENDAR