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DTSTART;TZID=UTC:20240304T130000
DTEND;TZID=UTC:20240314T151500
DTSTAMP:20260710T204149
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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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DTSTART;TZID=Europe/Helsinki:20240314T093000
DTEND;TZID=Europe/Helsinki:20240314T103000
DTSTAMP:20260710T204149
CREATED:20240308T153107Z
LAST-MODIFIED:20240308T153107Z
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SUMMARY:Kevin Munger (Pennsylvania State University) - Political Deepfakes Are As Credible As Other Fake Media And (Sometimes) Real Media
DESCRIPTION:[vc_row][vc_column][vc_column_text]\nSociology seminar – Thursdays\nTime: 9:30 am – 10:30 am \nDate: 14th March\nPlace: room 3060 \n  \nKevin Munger (Pennsylvania State University) – Political Deepfakes Are As Credible As Other Fake Media And (Sometimes) Real Media\nAbstract: \nWe demonstrate that fabricated videos of public officials synthesized by deep learning (“deepfakes”) are credible to a large portion of the American public – up to 50% of a representative sample of 5\,750 subjects – however no more than equivalent misinformation in extant modalities like text headlines or audio recordings. Moreover\, there are no meaningful heterogeneities in these credibility perceptions nor greater affective responses relative to other mediums across subgroups. However\, when asked to discern real videos from deepfakes\, partisanship explains a large gap in viewers’ detection accuracy\, but only for real videos\, not deepfakes. Brief informational messages or accuracy primes only sometimes (and somewhat) attenuate deepfakes’ effects. Above all else\, broader literacy in politics and digital technology increases discernment between deepfakes and authentic videos of political elites. Our findings come from two experiments testing exposure to a novel collection of deepfakes created in collaboration with tech industry partners. \n  \n  \n  \nOrganizers: Annina Cleasson\, Paola Tubaro\, Patrick Präg (CREST Sociology unit) \n  \nSponsors: CREST \n[/vc_column_text][/vc_column][/vc_row]\n
URL:https://crest.science/event/kevin-munger-pennsylvania-state-university-political-deepfakes-are-as-credible-as-other-fake-media-and-sometimes-real-media/
CATEGORIES:Seminars,Sociology
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DTSTART;TZID=Europe/Helsinki:20240314T120000
DTEND;TZID=Europe/Helsinki:20240314T133000
DTSTAMP:20260710T204149
CREATED:20240118T075739Z
LAST-MODIFIED:20240118T134712Z
UID:16498-1710417600-1710423000@crest.science
SUMMARY:Marissa Thompson (Columbia University) - They have Black in their blood”: Exploring how genetic ancestry tests affect racial appraisals and classifications
DESCRIPTION:Sociology seminar – Thursdays\nTime: 12:00 pm – 1:30 pm \nDate: 14th March 2024\nPlace: room 3105 \nZOOM LINK: https://zoom.us/j/94347463321?pwd=Z1BoT3ViY1V3TUVzcFQ1azRQckdDZz09 \nMarissa Thompson (Columbia University) – They have Black in their blood”: Exploring how genetic ancestry tests affect racial appraisals and classifications\nAbstract: \nHow do genetic ancestry tests (GATs) affect how Black Americans decide when others can – or cannot – identify as Black? This study explores the role of GATs in shaping racial appraisal and classification logics. Using a pre-registered nationally representative survey experiment that integrates causal inference with computational text analysis\, we disentangle how ancestry (as measured by a GAT) affects how U.S.-born Black Americans draw boundaries around group membership and how these effects vary across setting and prior identification. We find that\, though higher levels of Sub-Saharan African ancestry predict higher likelihoods of approval and classification as Black\, even individuals with low levels of such ancestry are likely to have their self-identification validated by respondents\, consistent with the practice of hypodescent. Furthermore\, ancestry treatment effects are primarily mediated by perceptions of the integrity of the individual’s self-identification\, suggesting that respondents believe there exists an underlying legitimate and honest way to identify that is partially based on one’s GAT result. However\, we also find that the aspects that affect approval and evaluations differ from those that affect classification; the ways that respondents selectively integrate different sources of information\, including ancestry\, occurs via a dual appraisal and classification process which we term racial contextualism.   \n  \nhttps://doi.org/10.31235/osf.io/8tnrk \n  \n  \nOrganizers: Annina Cleasson\, Paola Tubaro\, Patrick Präg (CREST Sociology unit) \n  \nSponsors: CREST \n  \n
URL:https://crest.science/event/marissa-thompson-columbia-university-they-have-black-in-their-blood-exploring-how-genetic-ancestry-tests-affect-racial-appraisals-and-classifications/
CATEGORIES:Seminars,Sociology
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DTSTART;TZID=Europe/Helsinki:20240314T140000
DTEND;TZID=Europe/Helsinki:20240314T151500
DTSTAMP:20260710T204149
CREATED:20240308T153312Z
LAST-MODIFIED:20240308T155306Z
UID:16801-1710424800-1710429300@crest.science
SUMMARY:Emmanuel PILLIAT (Université Montpellier) - Ranking the rows of a Permuted Isotonic Matrix Optimally and in Polynomial Time
DESCRIPTION:[vc_row][vc_column][vc_column_text]Statistical Seminar: Every Monday at 2:00 pm.\nTime: 2:00 pm – 3:15 pm\nDate: 14th March 2024\nPlace : 3001 \n  \nEmmanuel PILLIAT (Université Montpellier) – Ranking the rows of a Permuted Isotonic Matrix Optimally and in Polynomial Time \n  \n  \nAbstract: \nIn the context of crowdsourcing\, a natural question is how accurately we can determine the ranking of experts (or workers) who are labelling some data. The same kind of question arises in tournaments\, where we might want to rank players based on the outcomes of games between pairs of players. We will consider a ranking problem where we have noisy observations from a matrix with isotonic columns whose rows have been permuted by some permutation π*. This encompasses many models\, including crowd-labeling and ranking in tournaments by pairwise comparisons. After the introduction of the statistical model and of the risk measures\, we will discuss the ideas for an optimal and polynomial-time procedure for recovering π*\, settling an open problem in Flammarion et al. (2019). The presented approach is based on iterative pairwise comparisons by suitable data-driven weighted means of the columns. \n  \nOrganizers:\nAnna KORBA (CREST)\, Karim LOUNICI (CMAP) \, Jaouad MOURTADA (CREST) \nSponsors:\nCREST-CMAP[/vc_column_text][/vc_column][/vc_row]\n
URL:https://crest.science/event/emmanuel-pilliat-to-be-announced/
CATEGORIES:Statistics
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