 On Multivariate Methods in Robust EconometricsJan Kalina Prague Economic Papers, 2012, vol. 2012, issue 1, 69-82 Abstract: 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-weighting 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. Keywords: least weighted squares; heteroscedasticity; multivariate statistics; model selection; diagnostics; computational aspects (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:prg:jnlpep:v:2012:y:2012:i:1:id:411:p:69-82 Ordering information: This journal article can be ordered from DOI: 10.18267/j.pep.411 Access Statistics for this article Prague Economic Papers is currently edited by Klára Pavlová More articles in Prague Economic Papers from Prague University of Economics and Business Contact information at EDIRC. |
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