0365534 20240103195650.220111103235959.9 000299733400005 80052687193 10.1080/02331881003768891 12 s. cav_un_epca*02551390233-1888455 (2011)497-508 Robustness Convergence Empirical distribution Heteroscedasticity cav_un_auth*0101225 Víšek Jan Ámos Ekonometrie Department of Econometrics E E UTIA-B Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2011/SI/visek-0365534.pdf GA402/09/0557 GA UK CZ cav_un_auth*0278673 CEZ:AV0Z10750506 Neglecting heteroscedasticity of error terms may imply a wrong identification of regression. Employment of (heteroscedasticity resistent) White’s estimator of covariance matrix of estimates of regression coefficients may lead to the correct decision about significance of individual explanatory variables under heteroscedasticity. However, White’s estimator of covariance matrix was established for LS-regression analysis (in the case when error terms are normally distributed, LS- and ML-analysis coincide and hence then White’s estimate of covariance matrix is available for ML-regression analysis, too). To establish White’s-type estimate for another estimator of regression coefficients requires Bahadur representation of the estimator in question, under heteroscedasticity of error terms. The derivation of Bahadur representation for other (robust) estimators requires some tools. 2012 BB 1 4 A 4a 20231122134718.2 http://hdl.handle.net/11104/0200758 STATISTICSPROBABILITY 0.759 0.114 >10.0 0.00218 0.585 575 44 0.593 Q3 26.977 40.948 Q3 Q4 2011 Soubory v repozitáři: Visek-0365534.pdf 80052687193 SCOPUS 000299733400005 WOS cav_un_epca*0255139 Statistics 0233-1888 1029-4910 Roč. 45 č. 5 2011 497 508