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DTSTART:20260329T010000
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DTSTART:20261025T010000
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DTSTART;TZID=Europe/Helsinki:20260316T121500
DTEND;TZID=Europe/Helsinki:20260316T133000
DTSTAMP:20260827T094106
CREATED:20260302T133156Z
LAST-MODIFIED:20260305T104626Z
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SUMMARY:Domenico GIANNONE (Johns Hopkins University) "Bayesian Inference in IV Regression"
DESCRIPTION:[vc_row][vc_column][vc_column_text]Macro seminar\nTime : 12h15 – 13h30 \nDate : 16th  March 2026 \nSalle 3001 \nDomenico GIANNONE (Johns Hopkins University) “Bayesian Inference in IV Regression” \nAbstract: It is well known that standard frequentist inference breaks down in IV regressions with weak instruments. Bayesian inference with diffuse priors suffers from the same problem. We show that the issue arises because flat priors on the first-stage coefficients overstate instrument strength. In contrast\, inference improves drastically when an uninformative prior is specified directly on the concentration parameter—the key nuisance parameter capturing instrument relevance. The  resulting Bayesian credible intervals are asymptotically equivalent to the frequentist confidence intervals based on conditioning approaches\, and remain robust to weak instruments.\n \nJoint work : Michele Lenza and Giorgio Primiceri \nOrganizer :  Alessandro RIBONI \n
URL:https://crest.science/event/domenico-giannone-johns-hopkins-university-bayesian-inference-in-iv-regression/
CATEGORIES:Macroeconomics,Seminars
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DTSTART;TZID=Europe/Helsinki:20260316T140000
DTEND;TZID=Europe/Helsinki:20260316T153000
DTSTAMP:20260827T094106
CREATED:20260223T091638Z
LAST-MODIFIED:20260310T121344Z
UID:18818-1773669600-1773675000@crest.science
SUMMARY:Gabor LUGOSI (Universitat Pompeu Fabra) - Structure learning and property testing in graphical models
DESCRIPTION:Statistical Seminar: Every Monday at 2:00 pm.\nTime: 2:00 pm – 3:00 pm\nDate: 16th March\nPlace: 3001 \n  \nGabor LUGOSI (Universitat Pompeu Fabra) – Structure learning and property testing in graphical models \n  \n Abstract:  \nThe dependence structure of high-dimensional distributions is often modeled by graphical models. The problem of learning the graph underlying such distributions has received a lot of attention in statistics and machine learning. In problems of very high dimension\, it is often too costly even to store the sample covariance matrix. We propose a model in which one can query single entries of the covariance matrix. We construct efficient algorithms for structure recovery in Gaussian graphical models with query complexity that is quasi-linear in the dimension. We present algorithms that work for trees and\, more generally\, for graphs of small treewidth. We also discuss hypothesis testing of properties of the underlying graph.\nThe talk is based on joint work with Sofiya Burova\, Francisco Calvillo\, Luc Devroye\, Jakub Truszkowski\, Vasiliki Velona\, and Piotr Zwiernik. \n  \nOrganizers: \nAnna KORBA (CREST)\, Vincent DIVOL (CREST) \, Jaouad MOURTADA (CREST) \nSponsors:\nCREST-CMAP \n
URL:https://crest.science/event/gabor-lugosi-universitat-pompeu-fabra-tba/
CATEGORIES:Seminars,Statistics
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