Methodological study demonstrates an efficient heteroscedastic variance estimator in Gauss-Markov models, highlighting reduced mean square error and computational economy.
We describe an estimator of heteroscedastic variances in the Gauss-Markov linear model where E(ε) = 0 and with σ i 2 and unknown. It may be thought of as an approximation to the MINQUE method which results in computational economy, positive estimates, and decreased mean square error. Properties of this almost unbiased estimator are stated. It is compared with other estimators, and extensions to more general models are discussed.
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Horn et al. (1975) studied this question.
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