A Comparison of Asymptotic Covariance Matrices of Adjusted Least Squares and Structural Least Squares in Error Ridden Polynomial Regression

Kukush, Alexander; Schneeweiß, Hans (2000): A Comparison of Asymptotic Covariance Matrices of Adjusted Least Squares and Structural Least Squares in Error Ridden Polynomial Regression. Collaborative Research Center 386, Discussion Paper 218

Abstract

A polynomial structural errors-in-variables model with normal underlying distributions is investigated. An asymptotic covariance matrix of the SLS estimator is computed, including the correcting terms which appear because in the score function the sample mean and the sample variance are plugged in. The ALS estimator is also considered, which does not need any assumption on the regressor distribution. The asymptotic covariance matrices of the two estimators are compared in border cases of small and of large errors. In both situations it turns out that under the normality assumption SLS is strictly more efficient than ALS.

• Item Type: Paper (Research Paper)
• Keywords: Polynomial regression; structural errors-in-variables model; asymptotic covariance matrix; efficiency
• Faculties: Mathematics, Computer Science and Statistics > Statistics > Collaborative Research Center 386; Special Research Fields > Special Research Field 386
• Subjects: 500 Science > 510 Mathematics
• Language: English