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X-WR-CALNAME:Department of Economics | IP Paris
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X-WR-CALDESC:Events for Department of Economics | IP Paris
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DTSTART;TZID=Europe/Helsinki:20250915T160000
DTEND;TZID=Europe/Helsinki:20250915T173000
DTSTAMP:20260411T102028
CREATED:20250908T100057Z
LAST-MODIFIED:20250908T100057Z
UID:15873-1757952000-1757957400@econ.ip-paris.fr
SUMMARY:Lucas RESENDE (CREST) - "Unbiased Estimation of Multi-Way Gravity Models\, with Philippe Choné (CREST) and Guillaume Lecué (CREST and ESSEC)
DESCRIPTION:PSE Seminar : \nTime: 16:00 pm – 17:30 pm\nDate: 15th of September\nRoom : 3001 \n  \nLucas RESENDE (CREST) – “Unbiased Estimation of Multi-Way Gravity Models\, with Philippe Choné (CREST) and Guillaume Lecué (CREST and ESSEC) \n  \nAbstract : \n“Maximum likelihood estimators\, such as the Poisson Pseudo-Maximum Likelihood (PPML)\, suffer from the incidental parameter problem: a bias in the estimation of structural parameters that arises from the joint estimation of structural and nuisance parameters. To address this issue in multi-way gravity models\, we propose a novel\, asymptotically unbiased estimator. Our method reframes the estimation as a series of classification tasks and is agnostic to both the number and structure of fixed effects. In sparse data environments — common in the network formation literature — it is also computationally faster than PPML. We provide empirical evidence that our estimator yields more accurate point estimates and confidence intervals than PPML and its bias-correction strategies. These improvements hold even under model misspecification and are more pronounced in sparse settings. While PPML remains competitive in dense\, low-dimensional data\, our approach offers a robust alternative for multi-way models that scales efficiently with sparsity”. \n  \nOrganizer :\nLaurent DAVEZIES (Pôle économie du CREST) \nSponsors:\nCREST \n
URL:https://econ.ip-paris.fr/event/https-crest-science-user-lucas-resende/
CATEGORIES:Paris Econometrics Seminar,Seminars
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