Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/104961
Title: A paradox in least-squares estimation of linear regression models
Authors: Bai, Z.D. 
Guo, M.
Keywords: Consistency
Least-squares estimate
Rth moment
Issue Date: 1-Apr-1999
Citation: Bai, Z.D.,Guo, M. (1999-04-01). A paradox in least-squares estimation of linear regression models. Statistics and Probability Letters 42 (2) : 167-174. ScholarBank@NUS Repository.
Abstract: 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 finite rth moment, for r ∈e [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. © 1999 Elsevier Science B.V. All rights reserved.
Source Title: Statistics and Probability Letters
URI: http://scholarbank.nus.edu.sg/handle/10635/104961
ISSN: 01677152
Appears in Collections:Staff Publications

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