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DTSTART;TZID=Europe/Helsinki:20231120T121500
DTEND;TZID=Europe/Helsinki:20231120T133000
DTSTAMP:20260819T221853
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SUMMARY:Elisabeth PROEHL (University of Amsterdam) "Existence and Uniqueness of Recursive Equilibria With Aggregate and Idiosyncratic Risk"
DESCRIPTION:Macro seminar\nTime : 12h15 – 13h30 \nDate : 20 Novembre 2023 \nSalle 3001 \nElisabeth PROEHL (University of Amsterdam) “Existence and Uniqueness of Recursive Equilibria With Aggregate and Idiosyncratic Risk” \nAbstract: In this paper\, I study the existence and uniqueness of recursive equilibria in economies with aggregate and idiosyncratic risk. Rather than relying on compactness to establish existence\, I exploit the monotonicity property of the equilibrium model and rely on arguments from convex analysis. This methodology does not only give rise to a convergent iterative procedure\, but more strikingly\, it also yields uniqueness. To illustrate my theoretical results\, I establish sufficient conditions for the existence and uniqueness of solutions to the stochastic growth model as in Krusell and Smith (1998) and the heterogeneous-agent exchange economy as in Huggett (1993) with aggregate risk. \nPablo WINANT (CREST) \n
URL:https://crest.science/event/elisabeth-proehl-university-of-amsterdam/
CATEGORIES:Macroeconomics,Seminars
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DTSTART;TZID=Europe/Helsinki:20231120T140000
DTEND;TZID=Europe/Helsinki:20231120T150000
DTSTAMP:20260819T221853
CREATED:20231025T131420Z
LAST-MODIFIED:20231117T085307Z
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SUMMARY:Anindya DE (University of Pennsylvania) - "Testing convex truncation"
DESCRIPTION:Statistical Seminar: Every Monday at 2:00 pm.\nTime: 2:00 pm – 3:00 pm\nDate: 20th November 2023\nPlace: 3001 \n  \nAnindya De (University of Pennsylvania) “Testing convex truncation” \n Abstract: We study the basic statistical problem of testing whether normally distributed n-dimensional data has been truncated\, i.e. altered by only retaining points that lie in some unknown truncation set S. As our main algorithmic results\, (1) We give a computationally efficient O(n)-sample algorithm that can distinguish the standard normal distribution from the normal conditioned on an unknown and arbitrary convex set S. (2) We give a different computationally efficient O(n)-sample algorithm that can distinguish the standard normal distribution from the normal conditioned on an unknown and arbitrary mixture of symmetric convex sets. \nThese results stand in sharp contrast with known results for learning or testing convex bodies with respect to the normal distribution or learning convex-truncated normal distributions\, where state-of-the-art algorithms require essentially n^{O(sqrt{n})} samples. An easy argument shows that no finite number of samples suffices to distinguish the normal from an unknown and arbitrary mixture of general (not necessarily symmetric) convex sets\, so no common generalization of results (1) and (2) above is possible. We also prove lower bounds on the sample complexity of distinguishing algorithms (computationally efficient or otherwise) for various classes of convex truncations; in some cases these lower bounds match our algorithms up to logarithmic or even constant factors. \nBased on joint work with : Shivam Nadimpalli and Rocco Servedio. \nLink zoom : https://zoom.us/j/99093379768?pwd=c3I3ejQxUkwrV3k0dTNXUHpyUGNIdz09 \nOrganizers: \nZCristina BUTUCEA (CREST)\, Karim LOUNICI (CMAP) \, Jaouad MOURTADA (CREST) \nSponsors:\nCREST-CMAP \n
URL:https://crest.science/event/anindya-de-university-of-pennsylvania-to-be-announced/
CATEGORIES:Seminars,Statistics
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