Biases on variances estimated on large data-sets

François Gardes (francois.gardes@univ-paris1.fr)
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François Gardes: Centre d'Economie de la Sorbonne, Paris School of Economics & Western Catholic University, https://cv.archives-ouvertes.fr/francois-gardes

Documents de travail du Centre d'Economie de la Sorbonne from Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne

Abstract: The inverse dependency of the estimated variances onver the sample size throws a fundamental question on the validity of the usual statistical methodology, since any hypothesis on the value of a coefficient can be tested negatively by increasing the size of the data-set. I suppose that large data-sets are characterized by a concentration of information on homogenous sub-populations, a spatial autocorrelation of the error terms and the covariates may bias the estimation of variances. Using the corrections of variances under spatial autocorrelation, we obtain variances comparable to an estimation on sub-samples (named efficient sub-samples) the sizes of which are sufficient to contain the information which gives rise to similar estimates to those obtained on the whole population. Moreover, the estimation on efficient data-sets does not necessitate the specification of the spatial autocorrelations which are supposed to bias the estimated variances

Keywords: dataset; estimated variance; spatial autocorrelation; grouped observations (search for similar items in EconPapers)
JEL-codes: C01 C12 C55 (search for similar items in EconPapers)
Pages: 14 pages
Date: 2021-03
New Economics Papers: this item is included in nep-isf
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http://mse.univ-paris1.fr/pub/mse/CES2021/21022.pdf (application/pdf)
https://halshs.archives-ouvertes.fr/halshs-03325118

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Persistent link: https://EconPapers.repec.org/RePEc:mse:cesdoc:21022

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