Determination of Estimators with Minimum Asymptotic Covariance Matrices
- 1 August 1993
- journal article
- research article
- Published by Cambridge University Press (CUP) in Econometric Theory
- Vol. 9 (4) , 633-648
- https://doi.org/10.1017/s026646660000801x
Abstract
We give a straightforward condition sufficient for determining the minimum asymptotic variance estimator in certain classes of estimators relevant to econometrics. These classes are relatively broad, as they include extremum estimation with smooth or nonsmooth objective functions; also, the rate of convergence to the asymptotic distribution is not required to ben−½. We present examples illustrating the content of our result. In particular, we apply our result to a class of weighted Huber estimators, and obtain, among other things, analogs of the generalized least-squares estimator for leastLp-estimation, 1 ≤p< ∞.Keywords
This publication has 40 references indexed in Scilit:
- Adaptive Efficient Weighted Least Squares With Dependent ObservationsPublished by Springer Nature ,1991
- A method for calculating bounds on the asymptotic covariance matrices of generalized method of moments estimatorsJournal of Econometrics, 1985
- Misspecified models with dependent observationsJournal of Econometrics, 1982
- Statistical EstimationPublished by Springer Nature ,1981
- The nonlinear two-stage least-squares estimatorJournal of Econometrics, 1974
- Asymptotic Properties of Maximum Likelihood Estimators in Some Nonstandard Cases, IIJournal of the American Statistical Association, 1973
- Asymptotic Properties of Maximum Likelihood Estimators in Some Nonstandard CasesJournal of the American Statistical Association, 1971
- A characterization of limiting distributions of regular estimatesProbability Theory and Related Fields, 1970
- Asymptotic efficiency of the maximum likelihood estimator)Annals of the Institute of Statistical Mathematics, 1966
- An Efficient Method of Estimating Seemingly Unrelated Regressions and Tests for Aggregation BiasJournal of the American Statistical Association, 1962