On Multivariate Methods in Robust Econometrics

0358519 - ÚI 2013 RIV CZ eng J - Journal Article
Kalina, Jan
On Multivariate Methods in Robust Econometrics.
Prague Economic Papers. Roč. 21, č. 1 (2012), s. 69-82. ISSN 1210-0455. E-ISSN 2336-730X
R&D Projects: GA MŠMT(CZ) 1M06014
Institutional research plan: CEZ:AV0Z10300504
Keywords : least weighted squares * heteroscedasticity * multivariate statistics * model selection * diagnostics * computational aspects
Subject RIV: BB - Applied Statistics, Operational Research
Impact factor: 0.561, year: 2012
http://www.vse.cz/pep/abstrakt.php?IDcl=411

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.
Permanent Link: http://hdl.handle.net/11104/0196538