The efficiency of estimating seemingly unrelated regression equations

DOI10.1007/BF02506367zbMath0221.62023OpenAlexW2146151147MaRDI QIDQ2547426

Yanyan Li

Publication date: 1970

Published in: Annals of the Institute of Statistical Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf02506367

Mathematics Subject Classification ID

Linear regression; mixed models (62J05)

Related Items

Tests for sphericity under correlated multivariate regression equations model ⋮ Efficiency properties of feasible generalized least squares estimators in SURE models under non-normal disturbances ⋮ SEPARATE VERSUS SYSTEM METHODS OF STEIN-RULE ESTIMATION IN SEEMINGLY UNRELATED REGRESSION MODELS ⋮ Large-sample approximations in seemingly unrelated regression equations ⋮ Computation of standard errors in seemingly unrelated regression equation models ⋮ The efficiency of an improved method of estimating seemingly unrelated regression equations ⋮ Disturbance variance estimation in simulataneous equations by k-class method ⋮ The bias of generalized double k-class estimators ⋮ A note on the efficiency of the Zellner's seemingly unrelated regressions estimator ⋮ Two-stage and three-stage least squares estimation of dispersion matrix of disturbances in simultaneous equations ⋮ Unnamed Item ⋮ Estimation of seemingly unrelated regression equations ⋮ On the minimum mean squared error estimators in a regression model ⋮ A method for approximations to the PDF's and CDF's of GLSE's and its application to the seemingly unrelated regression model

Cites Work

• Unnamed Item
• On the estimation of generalized linear probability model involving discrete random variables
• Estimators for Seemingly Unrelated Regression Equations: Some Exact Finite Sample Results
• The Unbiasedness of Zellner's Seemingly Unrelated Regression Equations Estimators
• Further Properties of Efficient Estimators for Seemingly Unrelated Regression Equations
• An Efficient Method of Estimating Seemingly Unrelated Regressions and Tests for Aggregation Bias
• On Stochastic Limit and Order Relationships