On Multivariate Methods in Robust Econometrics
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.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Volume (Year): 2012 (2012)
Issue (Month): 1 ()
|Contact details of provider:|| Postal: nam. W. Churchilla 4, 130 67 Praha 3|
Phone: (02) 24 09 51 11
Fax: (02) 24 22 06 57
Web page: http://www.vse.cz/
More information through EDIRC
|Order Information:|| Postal: Editorial office Prague Economic Papers, University of Economics, nám. W. Churchilla 4, 130 67 Praha 3, Czech Republic|
Web: http://www.vse.cz/pep/ Email:
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Jeffrey M. Wooldridge, 2001. "Applications of Generalized Method of Moments Estimation," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 87-100, Fall.
- Ronchetti, Elvezio & Trojani, Fabio, 2001. "Robust inference with GMM estimators," Journal of Econometrics, Elsevier, vol. 101(1), pages 37-69, March.
- Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-1054, July.
- Cragg, John G, 1983. "More Efficient Estimation in the Presence of Heteroscedasticity of Unknown Form," Econometrica, Econometric Society, vol. 51(3), pages 751-763, May.
- Wagenvoort, Rien & Waldmann, Robert, 2002. "On B-robust instrumental variable estimation of the linear model with panel data," Journal of Econometrics, Elsevier, vol. 106(2), pages 297-324, February.
- Marco Riani & Anthony C. Atkinson & Andrea Cerioli, 2009.
"Finding an unknown number of multivariate outliers,"
LSE Research Online Documents on Economics
30462, London School of Economics and Political Science, LSE Library.
- Marco Riani & Anthony C. Atkinson & Andrea Cerioli, 2009. "Finding an unknown number of multivariate outliers," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 447-466.
- Garcia-Escudero, Luis Angel & Gordaliza, Alfonso, 2005. "Generalized Radius Processes for Elliptically Contoured Distributions," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 1036-1045, September.
When requesting a correction, please mention this item's handle: RePEc:prg:jnlpep:v:2012:y:2012:i:1:id:411:p:69-82. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Frantisek Sokolovsky)
If references are entirely missing, you can add them using this form.