Mixed regression estimator under misspecification

DOI10.1016/0165-1765(86)90115-1zbMath1328.62621OpenAlexW2007777550MaRDI QIDQ899790

Krishna Kadiyala

Publication date: 1 January 2016

Published in: Economics Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0165-1765(86)90115-1

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Linear regression; mixed models (62J05)

Related Items (10)

THE SMALL SAMPLE PROPERTIES OF THE PRINCIPAL COMPONENTS ESTIMATOR FOR REGRESSION COEFFICIENTS ⋮ Mean square error matrix superiority of empirical Bayes estimators under misspecification ⋮ Stochastic restricted biased estimators in misspecified regression model with incomplete prior information ⋮ Performance of the principal component two-parameter estimator in misspecified linear regression model ⋮ A comparison between two competing fixed parameter constrained general linear models with new regressors ⋮ Mean square error matrix superiority of estimators under linear restrictions and misspecification ⋮ Statistical analysis of a linear regression model with restrictions and superfluous variables ⋮ Mixed regression estimator under inclusion of some superfluous variables ⋮ r-d Class Estimator Under Misspecification ⋮ Superiority of the r-k class estimator over some estimators in a misspecified linear model

Cites Work

• A Comparison of Traditional and Stein-Rule Estimators under Weighted Squared Error Loss
• Optimal Critical Values for Pre-Testing in Regression
• Specification Errors and the Estimation of Economic Relationships
• Weaker Criteria and Tests for Linear Restrictions in Regression

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