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DTSTART;TZID=Europe/Helsinki:20240304T120000
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SUMMARY:Séminaire Palaisien
DESCRIPTION:REGISTER05 Mar 2024\, 12h At Inria – Salle Gilles Kahn\n\n\n\nAustin Stromme – Minimum intrinsic dimension scaling for entropic optimal transport\n \n\nEntropic optimal transport (entropic OT) is a regularized variant of the optimal transport problem\, widely used in practice for its computational benefits. A key statistical question for both entropic and un-regularized OT is the extent to which low-dimensional structure\, of the type conjectured by the well-known manifold hypothesis\, affects the statistical rates of convergence. In this talk\, we will present statistical results for entropic OT which clarify the statistical role of…\n\n\n\nBadr-Eddine Cherief Abdellatif – A PAC-Bayes perspective on learning and generalization\n \n\nBorn in the late 20th century\, PAC-Bayes has recently re-emerged as a powerful framework for learning with guarantees. Its bounds offer a principled way to understand the generalization ability of randomized learning algorithms\, even guiding the design of new ones. This introduction dives into the foundations of PAC-Bayes\, explores its recent advancements\, and tries to offer some insights into promising future research directions.\n\n\n\n
URL:https://crest.science/event/seminaire-palaisien-3/
LOCATION:INRIA – Salle Gilles Kahn
CATEGORIES:Conferences and Workshops,Statistics
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DTSTART;TZID=Europe/Helsinki:20240304T121500
DTEND;TZID=Europe/Helsinki:20240304T133000
DTSTAMP:20260711T003027
CREATED:20240131T073736Z
LAST-MODIFIED:20240131T074418Z
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SUMMARY:Antoine BERTHEAU (Norwegian School of Economics ) "Why Firms Lay Off Workers Instead of Cutting Wages: Evidence From Linked Survey-Administrative Data"
DESCRIPTION:Macro seminar\nTime : 12h15 – 13h30 \nDate : 04 Mars 2024 \nSalle 3001 \nAntoine BERTHEAU (Norwegian School of Economics ) “Why Firms Lay Off Workers Instead of Cutting Wages: Evidence From Linked Survey-Administrative Data” \nAbstract: We study how firms adjust labor in response to adverse shocks—via layoffs or pay cuts—and the reasons behind each adjustment margin. To do so\, we design and implement a novel large-scale survey of firms in Denmark and link it to administrative data. We find that layoffs are much more prevalent than pay cuts\, but pay cuts are not rare. Second\, employers do not consider pay cuts to be a viable substitute for layoffs during crises. The size of a hypothetical pay cut needed to save a layoff is large. Furthermore\, some layoffs during a crisis are not caused by the crisis. Rather\, a crisis is an opportune time for firms to lay off some workers. Third\, employers that do not cut base pay think that it would damage morale or lead employees to quit\, or they perceive base pay as a commitment to employees. Fourth\, losing specific workers’ skills is the key consideration in layoff decisions. \nOrganizers : Alice LAPEYRE (CREST) – Arne ULHENDORFF (CREST) \n
URL:https://crest.science/event/antoine-bertheau-norwegian-school-of-economics-why-firms-lay-off-workers-instead-of-cutting-wages-evidence-from-linked-survey-administrative-data/
CATEGORIES:Macroeconomics,Seminars
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DTSTART;TZID=UTC:20240304T130000
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SUMMARY:Dynamic Factor Models\, Matteo Barigozzi (Università di Bologna)
DESCRIPTION:  \n\n\n\n  \n  \nSCHEDULE\n  \nMonday\n  \n4th March 2024 \n11th March 2024\n  \nFrom 13:00 to 16:45\n  \nRoom 2033\n\n\n  \nThursday\n  \n7th March 2024\n  \nFrom 13:00 to 16:15\n  \nRoom 2033\n\n\n\nAims and objectives\nThe aim of this course is to provide an introduction to factor models in time series analysis by teaching students the basic theoretical foundations and by illustrating  them some applications to macroeconomics and finance. \nIn the last years large datasets have become increasingly available to researchers and practitioners in many disciplines. In particular\, during this big data revolution the analysis of high-dimensional time series has become one of the most active subjects of modern statistical methodology with applications in the most different areas of science including finance\, econometrics\, meteorology\, genomics\, chemometrics\, complex physics simulations\, biological and environmental research. Although the value of information is unquestionable\, the possibility of extracting meaningful and useful information out of this large amount of data is also of great importance. To achieve such dimension reduction\, several new analytical and computational techniques have been developed under the name of machine learning methods. Among these factor models not only are one of the pioneering methods in the field of unsupervised learning (dating back to Spearman\, 1904)\, but up to these days have also been one of the most popular and most employed ones. \nWe start by discussing principal component analysis as a useful dimension reduction technique for large panels of time series. This is the most simple example of factor model (the static model) which we then generalize to include all temporal relations among the considered variables (the dynamic model). We then focus on the case in which the dynamic model can be re–written as a state space model and we present its estimation via Kalman filter and the Expectation Maximization algorithm. We then consider application of these models in two fields. First\, in macroeconometrics for building indicators of the business cycle\, for nowcasting\, and for policy analysis problems. Related to these we briefly discuss how to deal with the issues of non–fundamentalness and cointegration. Second\, in financial econometrics for volatility modelling and forecasting. Related to these we briefly discuss the complementarity of factor and network models and the issue of conditional heteroskedasticity. Real–data applications taken from existing papers are discussed during the lectures. Matlab or R code will be provided. \n\n
URL:https://crest.science/event/dynamic-factor-models-matteo-barigozzi-universita-di-bologna/
LOCATION:2033
CATEGORIES:Doctoral Courses,Finance
ORGANIZER;CN="Jean-Michel%20Zakoian":MAILTO:zakoian@ensae.fr
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