BEGIN:VCALENDAR VERSION:2.0 PRODID:-//CREST - ECPv5.1.3//NONSGML v1.0//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-WR-CALNAME:CREST X-ORIGINAL-URL:https://crest.science X-WR-CALDESC:Events for CREST BEGIN:VTIMEZONE TZID:Europe/Helsinki BEGIN:DAYLIGHT TZOFFSETFROM:+0200 TZOFFSETTO:+0300 TZNAME:EEST DTSTART:20240331T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0300 TZOFFSETTO:+0200 TZNAME:EET DTSTART:20241027T010000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTART;VALUE=DATE:20241209 DTEND;VALUE=DATE:20241210 DTSTAMP:20260715T155327 CREATED:20240514T053739Z LAST-MODIFIED:20240514T053739Z UID:17016-1733702400-1733788799@crest.science SUMMARY:Workshop Empirical Monetary Economics DESCRIPTION:December 9\,2024 \nScientific committee: Giovanni Ricco \nLocation: OFCE (Google Maps)      10 place de Catalogne\, 75014 Paris\, France \nhttps://sites.google.com/site/workshopeme/ \n URL:https://crest.science/event/workshop-empirical-monetary-economics/ CATEGORIES:Conferences and Workshops,Economics END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Helsinki:20241209T121500 DTEND;TZID=Europe/Helsinki:20241209T133000 DTSTAMP:20260715T155327 CREATED:20241205T090656Z LAST-MODIFIED:20241205T090656Z UID:17629-1733746500-1733751000@crest.science SUMMARY:Jack WILLIS (Columbia) "Land Rental Markets: Experimental Evidence from Kenya" DESCRIPTION:[vc_row][vc_column][vc_column_text]Macro seminar\nTime : 12h15 – 13h30 \nDate : 18th  December 2024 \nSalle 3001 \nJack WILLIS (Columbia) “Land Rental Markets: Experimental Evidence from Kenya” \nAbstract: Do land market frictions cause misallocation in agriculture? In a field experiment in Western Kenya\, we randomly subsidize owners to rent out land. Induced rentals mostly persist after the subsidy ends and increase output and value added\, consistent with misallocation. Gains from trade arise from renters choosing higher-value crops\, having higher productivity\, and adopting more non-labor inputs\, while\, perhaps surprisingly\, renters use similar quantities of labor as owners. Induced rentals are not those with the largest predicted gains\, underlining the importance of the joint distribution of gains and frictions\, with frictions arising from search\, risk\, and learning. \nJoint work : Michelle Acampora and Lorenzo Casaburi \nOrganizer : Jean-Baptiste MICHAU \n URL:https://crest.science/event/jack-willis-columbia-land-rental-markets-experimental-evidence-from-kenya/ CATEGORIES:Macroeconomics,Seminars ATTACH;FMTTYPE=: END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Helsinki:20241209T140000 DTEND;TZID=Europe/Helsinki:20241209T153000 DTSTAMP:20260715T155327 CREATED:20241202T161157Z LAST-MODIFIED:20241202T161225Z UID:17613-1733752800-1733758200@crest.science SUMMARY:Pragya SUR (Harvard University​) - Spectrum-Aware Debiasing: A Modern Inference Framework with Applications to Principal Components Regression DESCRIPTION:Statistical Seminar: Every Monday at 2:00 pm.\nTime: 2:00 pm – 3:00 pm\nDate: 9th December\nPlace: 3001 \n  \nPragya SUR (Harvard University​) – Spectrum-Aware Debiasing: A Modern Inference Framework with Applications to Principal Components Regression \n  \n Abstract:  \nDebiasing methodologies have emerged as powerful tools for making statistical inferences in high-dimensional problems. Since its original introduction\, the methodology underwent a major development with the introduction of debiasing techniques that adjust for degrees-of-freedom (Bellec and Zhang\, 2019). While overcoming limitations of initial debiasing approaches\, this updated method relies on Gaussian/sub-Gaussian tailed designs and independent\, identically distributed samples – a key limitation. In this talk\, I will propose a novel debiasing formula that breaks this barrier by exploiting the spectrum of the sample covariance matrix. Our formula applies to a much broader class of designs\, including some heavy- tailed distributions\, as well as certain dependent data settings. Our correction term differs significantly from prior work but recovers the Gaussian-based formula as a special case. Notably\, our approach does not require estimating the high-dimensional population covariance matrix yet can account for certain classes of dependence among both features and samples. We demonstrate the utility of our method for several statistical inference problems. As a by-product\, our work also introduces the first debiased principal component regression estimator with formal guarantees in high dimensions. \n  \nOrganizers: \nAnna KORBA (CREST)\, Karim LOUNICI (CMAP) \, Jaouad MOURTADA (CREST) \nSponsors:\nCREST-CMAP \n URL:https://crest.science/event/pragya-sur-spectrum-aware-debiasing-a-modern-inference-framework-with-applications-to-principal-components-regression/ CATEGORIES:Seminars,Statistics ATTACH;FMTTYPE=: END:VEVENT END:VCALENDAR