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A paradox in least-squares estimation of linear regression models

✍ Scribed by Z.D. Bai; Meihui Guo

Publisher
Elsevier Science
Year
1999
Tongue
English
Weight
97 KB
Volume
42
Category
Article
ISSN
0167-7152

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✦ Synopsis

This note considers a paradox arising in the least-squares estimation of linear regression models in which the error terms are assumed to be i.i.d. and possess ÿnite rth moment, for r ∈ [1; 2). We give a concrete example to show that the least-squares estimator of the slope parameter is inconsistent when the intercept parameter of the model is given. However, surprisingly this estimator is consistent when the intercept parameter is intendedly assumed to be unknown and re-estimated simultaneously with the slope parameter.

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Linear Least Squares Estimation of Regression Models for Two-Dimensional Random Fields
✍ Guy Cohen; Joseph M. Francos πŸ“‚ Article πŸ“… 2002 πŸ› Elsevier Science 🌐 English βš– 122 KB

We consider the problem of estimating regression models of two-dimensional random fields. Asymptotic properties of the least squares estimator of the linear regression coefficients are studied for the case where the disturbance is a homogeneous random field with an absolutely continuous spectral dis