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DTSTART:20220327T010000
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DTSTART:20221030T010000
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DTSTART;TZID=Europe/Helsinki:20220630T103000
DTEND;TZID=Europe/Helsinki:20220630T113000
DTSTAMP:20260711T160357
CREATED:20220623T115407Z
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UID:13810-1656585000-1656588600@crest.science
SUMMARY:Davide LA VECCHIA (Geneve University) "STATISTICAL ANALYSIS OF NETWORK DATA: LEARNING FROM SMALL SAMPLES (WITH A COUPLE OF DETOURS)"
DESCRIPTION:The Financial Econometrics Seminar: \nTime: 10:30 pm\nDate: 30th of June 2022\nRoom 3001 \nDavide LA VECCHIA (Geneve University) “STATISTICAL ANALYSIS OF NETWORK DATA: LEARNING FROM SMALL SAMPLES (WITH A COUPLE OF DETOURS)” \nAbstract :The talk introduces new higher-order asymptotic techniques for the Gaussian maximum likelihood estimator in a spatial panel data model\, with xed e ects\, time-varying covariates\, and spatially correlated errors. The proposed saddlepoint density and tail area approximation feature relative error of order O(1=(n(T ^ 1))) with n being the cross-sectional dimension and T the time-series dimension. The main theoretical tool is the tilted-Edgeworth technique in a non-identically distributed setting. The density approximation is always non-negative\, does not need resampling\, and is accurate in the tails. Monte Carlo experiments on density approximation and testing in the presence of nuisance parameters illustrate the good performance of the novel approximation over rst-order asymptotics and Edgeworth expansion. An empirical application to the investment-saving relationship in OECD (Organisation for Economic Co-operation and Development) countries shows disagreement between testing results based on first-order asymptotics and saddlepoint techniques. \nOrganizers:\n\nJean-Michel ZAKOIAN  (CREST) \nSponsors:\nCREST \n
URL:https://crest.science/event/davide-la-vecchia-geneve-university-statistical-analysis-of-network-data-learning-from-small-samples-with-a-couple-of-detours/
CATEGORIES:Finance-Insurance,Financial Econometrics
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