The use of incomplete observations in multiple regression analysis. A generalized least squares approach

DOI10.1016/0304-4076(73)90018-3zbMath0294.62078OpenAlexW1600292947MaRDI QIDQ1212764

Marcel G. Dagenais

Publication date: 1973

Published in: Journal of Econometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0304-4076(73)90018-3

Mathematics Subject Classification ID

Asymptotic distribution theory in statistics (62E20) Linear regression; mixed models (62J05) Monte Carlo methods (65C05)

Related Items (8)

Efficient estimation with missing data and endogeneity ⋮ Missing regressor values under conditions of multicollinearity ⋮ Linear regression using both temporally aggregated and temporally disaggregated data ⋮ Linear regression using both temporally aggregated and temporally disaggregated data ⋮ Incomplete observations and simultaneous-equations models ⋮ Small sample estimation of regression parameters in the three‐variable linear model, with incomplete observations ⋮ Regression using mixed annual and quarterly data ⋮ Weighted modified first order regression procedures for estimation in linear models with missing \(X\)-observations

Cites Work

• Optimal Strategy for Item Presentation in a Learning Process
• Further Suggestions Concerning the Utilization of Incomplete Observations in Regression Analysis
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