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On Multivariate Methods in Robust Econometrics
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SYSNO ASEP 0358519 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title On Multivariate Methods in Robust Econometrics Author(s) Kalina, Jan (UIVT-O) RID, SAI, ORCID Source Title Prague Economic Papers. - : Vysoká škola ekonomická v Praze - ISSN 1210-0455
Roč. 21, č. 1 (2012), s. 69-82Number of pages 14 s. Language eng - English Country CZ - Czech Republic Keywords least weighted squares ; heteroscedasticity ; multivariate statistics ; model selection ; diagnostics ; computational aspects Subject RIV BB - Applied Statistics, Operational Research R&D Projects 1M06014 GA MŠMT - Ministry of Education, Youth and Sports (MEYS) CEZ AV0Z10300504 - UIVT-O (2005-2011) UT WOS 000303301400005 EID SCOPUS 84859460598 Annotation This work studies implicitly weighted robust statistical methods suitable for econometric problems. We study robust estimation mainly for the context of heteroscedasticity or high dimension, which are up-to-date topics of current econometrics. We describe a modification of linear regression resistant to heteroscedasticity and study its computational aspects. For a robust version of the instrumental variables estimator we propose an asymptotic test of heteroscedasticity. Further we describe robust statistical methods for dimension reduction and classification analysis. We propose the robust quadratic classification analysis based on a new minimum weighted covariance determinant (MWCD) estimator. In general the robust methods based on down-weightening less reliable observations are resistant to outlying values (outliers) and insensitive to the assumption of Gaussian normal distribution of the data. The methods are illustrated on econometric data examples. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2013 Electronic address http://www.vse.cz/pep/abstrakt.php?IDcl=411
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