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X-WR-CALDESC:Events for CREST
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DTSTART:20230326T010000
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DTSTART;TZID=Europe/Helsinki:20231204T121500
DTEND;TZID=Europe/Helsinki:20231204T133000
DTSTAMP:20260711T024301
CREATED:20231016T101532Z
LAST-MODIFIED:20231127T131859Z
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SUMMARY:Sophocles MAVROEIDIS (Oxford) "Testing the effectiveness of unconventional monetary policy in Japan and the United States"
DESCRIPTION:Macro seminar\nTime : 12h15 – 13h30 \nDate : 04 Décembre 2023 \nSalle 3001 \n  \nSophocles MAVROEIDIS (Oxford) “Testing the effectiveness of unconventional monetary policy in Japan and the United States” \nAbstract:  irrelevant. We develop a theoretical model that underpins our empirical test of this irrelevance hypothesis based on the simple idea that under the hypothesis\, the short rate can be excluded in any empirical model that accounts for alternative measures of monetary policy. We test the hypothesis for Japan and the United States using a structural vector autoregressive model with an effective lower bound on the policy rate. We firmly reject the hypothesis but find that unconventional policy has had strong delayed effects.Unconventional monetary policy may make the effective lower bound on the short-term interest rate \n  \n
URL:https://crest.science/event/sophocles-mauroeidis-oxford-t-b-a/
CATEGORIES:Macroeconomics,Seminars
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DTSTART;TZID=Europe/Helsinki:20231204T150000
DTEND;TZID=Europe/Helsinki:20231204T163000
DTSTAMP:20260711T024301
CREATED:20231129T110623Z
LAST-MODIFIED:20231129T120434Z
UID:16280-1701702000-1701707400@crest.science
SUMMARY:Anindya DE (University of Pennsylvania) - "Testing convex truncation"
DESCRIPTION:Statistical Seminar: Every Monday at 2:00 pm.\nTime: 3:00 pm – 4:00 pm\nDate: 4th December 2023\nPlace: ZOOM \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/98972999889?pwd=OWI2QkdTWDJDQUgvSUN1ZGhGWV \nOrganizers: \nZCristina BUTUCEA (CREST)\, Karim LOUNICI (CMAP) \, Jaouad MOURTADA (CREST) \nSponsors:\nCREST-CMAP \n
URL:https://crest.science/event/anindya-de-university-of-pennsylvania-testing-convex-truncation/
CATEGORIES:Seminars,Statistics
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DTSTART;TZID=Europe/Helsinki:20231204T161500
DTEND;TZID=Europe/Helsinki:20231204T173000
DTSTAMP:20260711T024301
CREATED:20231012T043114Z
LAST-MODIFIED:20231201T125840Z
UID:16062-1701706500-1701711000@crest.science
SUMMARY:Jean-Jacques FORNERON (Boston University) - "Occasionally Misspecified"
DESCRIPTION:Paris Econometrics Seminar CREST – PSE – Sciences Po\nTime: 04:15 pm – 05:30 pm\nDate: 4th of  December\nZoom \nJean-Jacques FORNERON (Boston University) – Occasionally Misspecified \nAbstract : When fitting a particular Economic model on a sample of data\, the model may turn out to be heavily misspecified for some observations. This can happen because of unmodelled idiosyncratic events\, such as an abrupt but short-lived change in policy. These outliers can significantly alter estimates and  inferences. A robust estimation is desirable to limit their influence. For skewed data\, this induces  another bias which can also invalidate the estimation and inferences. This paper proposes a robust GMM estimator with a simple bias correction that does not degrade robustness significantly. The paper provides finite-sample robustness bounds\, and asymptotic uniform equivalence with an oracle that  discards all outliers. Consistency and asymptotic normality ensue from that result. An application to the “Price-Puzzle\,” which finds inflation increases when monetary policy tightens\, illustrates the concerns and the method. The proposed estimator finds the intuitive result: tighter monetary policy leads to a decline in inflation.\n \n  \nOrganizers:\nElia Lapenta – CREST/ENSAE\nPhilipp Ketz – CNRS/PSE\nClément de Chaisemartin – Sciences Po \nSponsors:\nCREST \n
URL:https://crest.science/event/jean-jacques-forneron-boston-university-t-b-a/
CATEGORIES:Paris Econometrics Seminar,Seminars
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