Maximum likelihood estimation of the GLS model with unknown parameters in the disturbance covariance matrix
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Publication:1251042
DOI10.1016/0304-4076(78)90056-8zbMath0389.62049OpenAlexW2142578491MaRDI QIDQ1251042
Publication date: 1978
Published in: Journal of Econometrics (Search for Journal in Brave)
Full work available at URL: https://ageconsearch.umn.edu/record/293034/files/amsterdam018.pdf
Applications of statistics to economics (62P20) Linear regression; mixed models (62J05) Point estimation (62F10)
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Cites Work
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- The commutation matrix: Some properties and applications
- Multivariate error components analysis of linear and nonlinear regression models by maximum likelihood
- Maximum probability estimators
- Transformations for Estimation of Linear Models with Nested-Error Structure
- A General Procedure for Obtaining Maximum Likelihood Estimates in Generalized Regression Models
- Asymptotic Properties of Maximum Likelihood Estimators in Some Nonstandard Cases, II
- Efficient Estimation of a System of Regression Equations when Disturbances are Both Serially and Contemporaneously Correlated
- The Unbiasedness of Zellner's Seemingly Unrelated Regression Equations Estimators
- A Transformation Used to Circumvent the Problem of Autocorrelation
- Some Theorems on Matrix Differentiation with Special Reference to Kronecker Matrix Products
- An Efficient Method of Estimating Seemingly Unrelated Regressions and Tests for Aggregation Bias
