This paper is concerned with the equivalence of the weighted least squares estimators (WLSE) and the generalised least squares estimators (GLSE). Necessary and su±cient conditions for the WLSE to be best linear unbiased esti- mators are derived, generalising Anderson (1948, 1971) theorem on the equivalence between the ordinary least squares estimators and the GLSE. Procedures for ob- taining the optimal kernel for a given covariance structure are also described, where optimality is to be intended in the Gauss-Markov sense. The results are illustrated in the context of local polynomial regression methods for the estimation of the underlying trend of a time series.
Luati A, Proietti T (2008). On the equivalence of the weighted least squares and the generalised least squares estimators. HEIDELBERG : Physica Verlag.
On the equivalence of the weighted least squares and the generalised least squares estimators
LUATI, ALESSANDRA;
2008
Abstract
This paper is concerned with the equivalence of the weighted least squares estimators (WLSE) and the generalised least squares estimators (GLSE). Necessary and su±cient conditions for the WLSE to be best linear unbiased esti- mators are derived, generalising Anderson (1948, 1971) theorem on the equivalence between the ordinary least squares estimators and the GLSE. Procedures for ob- taining the optimal kernel for a given covariance structure are also described, where optimality is to be intended in the Gauss-Markov sense. The results are illustrated in the context of local polynomial regression methods for the estimation of the underlying trend of a time series.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.