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X-WR-CALNAME:CREST
X-ORIGINAL-URL:https://crest.science
X-WR-CALDESC:Events for CREST
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TZID:Europe/Helsinki
BEGIN:DAYLIGHT
TZOFFSETFROM:+0200
TZOFFSETTO:+0300
TZNAME:EEST
DTSTART:20250330T010000
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DTSTART:20251026T010000
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DTSTART;VALUE=DATE:20250331
DTEND;VALUE=DATE:20250401
DTSTAMP:20260710T073428
CREATED:20250106T140435Z
LAST-MODIFIED:20250106T140435Z
UID:17722-1743379200-1743465599@crest.science
SUMMARY:Dimitrios Xefteris (University of Cyprus): "Information Aggregation by Voting and Costly Political Influence"
DESCRIPTION:Doctoral course: “Information Aggregation by Voting and Costly Political Influence” \n24/03/2025 – 26/03/2025 – 31/03/2025 – 02/04/2025 \nReferent: Matias Nunez \n
URL:https://crest.science/event/dimitrios-xefteris-university-of-cyprus-information-aggregation-by-voting-and-costly-political-influence-4/
CATEGORIES:Doctoral Courses,Economics
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BEGIN:VEVENT
DTSTART;VALUE=DATE:20250331
DTEND;VALUE=DATE:20250401
DTSTAMP:20260710T073428
CREATED:20250106T140649Z
LAST-MODIFIED:20250106T140649Z
UID:17727-1743379200-1743465599@crest.science
SUMMARY:Alessandra Luati (Imperial College London): "Inference in Time-Varying Parameter Models"
DESCRIPTION:Alessandra Luati (Imperial College London): “Inference in Time-Varying Parameter Models \n31/03/2025 – 03/04/2025 – 07/04/2025 – 10/04/2025 \nReferent: Christian Francq \n
URL:https://crest.science/event/alessandra-luati-imperial-college-london-inference-in-time-varying-parameter-models-2/
CATEGORIES:Doctoral Courses,Finance
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Helsinki:20250331T121500
DTEND;TZID=Europe/Helsinki:20250331T133000
DTSTAMP:20260710T073428
CREATED:20250321T095214Z
LAST-MODIFIED:20250325T075007Z
UID:17976-1743423300-1743427800@crest.science
SUMMARY:Paraskevi PAPPA (Universidad Carlos III de Madrid) "Understanding the Transatlantic Divide in Labor Income Shares"
DESCRIPTION:[vc_row][vc_column][vc_column_text]Macro seminar\nTime : 12h15 – 13h30 \nDate : 31th  March 2025 \nSalle 3001 \nParaskevi PAPPA (Universidad Carlos III de Madrid) “Understanding the Transatlantic Divide in Labor Income Shares” \nAbstract: This paper analyzes medium-to-long-term labor share trends in the US and major Euro Area countries from 1960 using a panel vector autoregression model with common trends and theory-based sign restrictions. We find distinct patterns across the Atlantic: Europe saw a rise in labor share until the 1980s\, followed by a large decline over the next two decades\, stabilizing at a lower level after 2000. In contrast\, the US experienced a sharp decline starting in 2000. In Europe\, labor market institutions\, like higher bargaining power of workers\, before the 1980s and increased labor supply thereafter—explain the decline. In the US\, labor-saving technological factors\, particularly automation\, rather than globalization\, have played a key role in the labor share decline.  \nJoint work : Drago Bergholt\, Francesco Furlanetto\, Nicolo Maffei-Faccioli and \n
URL:https://crest.science/event/paraskevi-pappa-universidad-carlos-iii-de-madrid-t-b-a/
CATEGORIES:Macroeconomics,Seminars
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BEGIN:VEVENT
DTSTART;TZID=Europe/Helsinki:20250331T140000
DTEND;TZID=Europe/Helsinki:20250331T140000
DTSTAMP:20260710T073428
CREATED:20250318T071551Z
LAST-MODIFIED:20250327T133149Z
UID:17966-1743429600-1743429600@crest.science
SUMMARY:Loucas PILLAUD-VIVIEN (Ecole des Ponts) - Learning Gausssian multi-index models via gradient flow
DESCRIPTION:Statistical Seminar: Every Monday at 2:00 pm.\nTime: 2:00 pm – 3:00 pm\nDate: 31th March\nPlace: 3001 \n  \nLoucas PILLAUD-VIVIEN – Learning Gausssian multi-index models via gradient flow \n  \n  \n Abstract:  \nWe study gradient flow on the multi-index regression problem for high-dimensional Gaussian data. Multi-index functions consist of a composition of an unknown low-rank linear projection and an arbitrary unknown\, low-dimensional link function. As such\, they constitute a natural template for feature learning in neural networks. We consider a two-timescale algorithm\, whereby the low-dimensional link function is learnt with a non-parametric model infinitely faster than the subspace parametrizing the low-rank projection. By appropriately exploiting the matrix semigroup structure arising over the subspace correlation matrices\, we establish global convergence of the resulting Grassmannian population gradient flow dynamics\, and provide a quantitative description of its associated ‘saddle-to-saddle’ dynamics. \n  \nOrganizers: \nAnna KORBA (CREST)\, Karim LOUNICI (CMAP) \, Jaouad MOURTADA (CREST) \nSponsors:\nCREST-CMAP \n
URL:https://crest.science/event/loucas-pillaud-vivien-ecole-des-ponts-tba/
CATEGORIES:Seminars,Statistics
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