Estimating Heteroscedastic Variances in Linear Models - A Simpler Approach.
Abstract
The authors describe an estimator of heteroscedastic variances in the Gauss-Markov linear model 7 = X beta + epsilon where E(epsilon) = O and Var (epsilon) = diag((Sigma sub 1, Sup 2),..., (Sigma sub n, sup 2)) with (Sigma sub i, sup 2) and beta unknown. It may be thought of as an approximation to the MINQUE method, but it results in both computational economy and decreased mean square error. Properties of this approximately unbiased estimator are stated and it is compared with other estimators. Extensions to more general models are discussed. (Author)
Document Details
- Document Type
- Technical Report
- Publication Date
- Jul 01, 1973
- Accession Number
- AD0765624
Entities
People
- David B. Duncan
- Roger A. Horn
- Susan D. Horn
Organizations
- Johns Hopkins University