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X-WR-CALDESC:Events for CREST
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DTSTART:20260329T010000
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DTSTART;TZID=Europe/Helsinki:20260914T121500
DTEND;TZID=Europe/Helsinki:20260914T133000
DTSTAMP:20260923T155712
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LAST-MODIFIED:20260817T130345Z
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SUMMARY:Simon BUNEL (INSEAD) "Cultivating Productivity through Regulation: The Case of Pesticide Bans in France"
DESCRIPTION:Séminaire Macro\n12h15 – 13h30 \n14 septembre 2026 \nSalle 3001 \nSimon BUNEL (INSEAD) “Cultivating Productivity through Regulation: The Case of Pesticide Bans in France” \nRésumé : \nThis paper examines the long-standing debate on regulation’s impact on productivity and innovation using novel French agricultural data combining farm-level balance sheets\, crop protection records\, and regulatory changes. The findings highlight a policy trade-off. On the one hand\, when regulation bans an active substance for which no substitute is available\, it causes short-term productivity declines that mitigation practices cannot offset.\nOn the other hand\, it can also act as a catalyst for innovation over the medium term. In particular\, such substitute-less bans spur agrochemical firms to develop new solutions including entirely new active molecules for the regulated uses. These results highlight the need for regulatory policies that balance short-term economic costs with long-term technological progress. \n
URL:https://crest.science/event/simon-bunel-insead-t-b-a/
LOCATION:3001
CATEGORIES:Macroeconomics,Seminars
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DTSTART;TZID=Europe/Helsinki:20260914T140000
DTEND;TZID=Europe/Helsinki:20260914T150000
DTSTAMP:20260923T155712
CREATED:20260909T082841Z
LAST-MODIFIED:20260909T082841Z
UID:19120-1789394400-1789398000@crest.science
SUMMARY:Richard NICKL (University of Cambridge) - Statistical Inference for infinite-dimensional dynamical systems
DESCRIPTION:Statistical Seminar: Every Monday at 2:00 pm.\nTime: 2:00 pm – 3:00 pm\nDate: 14th September\nPlace: 3001 \n  \nRichard NICKL (University of Cambridge) – Statistical Inference for infinite-dimensional dynamical systems \n  \n Abstract:  \nWe study optimal statistical inference procedures for the states of time evolution phenomena occurring in `data assimilation’ or filtering problems. There it is a common practice to assign a Gaussian process prior on the initial condition of a dynamical system and to update it to a Bayesian posterior measure in the space of possible trajectories given a discrete sample of the process. In key applications the dynamics are non-linear\, such as with Navier-Stokes equations in geophysical sciences or reaction-diffusion equations in biochemistry. While Bayesian posterior distributions are widely computed by filtering or MCMC methods\, little is known about the statistical behaviour of these posterior measures in non-linear settings. In this talk we will introduce a theoretical framework for such models and then present recent results\, known as `Bernstein-von Mises theorems’\, that show that the posterior measures are approximated in function space by the Gaussian laws of solutions to certain SPDEs that involve the inverse Fisher information of the underlying statistical model. \n  \n  \nOrganizers: \nAnna KORBA (CREST)\, Vincent DIVOL (CREST)\, Jaouad MOURTADA (CREST) \n  \n  \nSponsors:\nCREST-CMAP \n
URL:https://crest.science/event/richard-nickl-university-of-cambridge-statistical-inference-for-infinite-dimensional-dynamical-systems/
CATEGORIES:Seminars,Statistics
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