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We compare the asymptotic covariance matrix of the ML estimator in a nonlinear measurement error model to the asymptotic covariance matrices of the CS and SQS estimators studied in Kukush et al (2002). For small measurement error variances they are equal up to the order of the measurement error...
Persistent link: https://www.econbiz.de/10010266158
We consider a polynomial regression model, where the covariate is measured with Gaussian errors. The measurement error variance is supposed to be known. The covariate is normally distributed with known mean and variance. Quasi Score (QS) and Corrected Score (CS) are two consistent estimation...
Persistent link: https://www.econbiz.de/10010266230
Hans Schneeweiß is- one of the best-known German econometricians and statisticians. He was born in Glatz, Silesia, on March 13, 1933. Hans SchneeweiB studied mathematics and physics and received his Ph. D. degree from the Johann-Wolfgang-Goethe University, Frankfurt, in 1960. He was member of...
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we prove that the quasi-score estimator in a mean-variance model is optimal in the class of (unbiased) linear score estimators, in the sense that the difference of the asymptotic covariance matrices of the linear score and quasi-score estimator is positive semi-definite. We also give conditions...
Persistent link: https://www.econbiz.de/10003310102
The paper is a survey of recent investigations by the authors and others into the relative efficiencies of structural and functional estimators of the regression parameters in a measurement error model. While structural methods, in particular the quasi-score (QS) method, take advantage of the...
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