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Toutenburg, Helge and Srivastava, V. K. (1998): Impact of Departure from Normality on the Efficiency of Estimating Regression Coefficients when Some Observations are Missing. Collaborative Research Center 386, Discussion Paper 130 [PDF, 273kB]


This article considers a linear regression model in which some observations on an explanatory variable are missing, and presents three least squares estimators for the regression coefficients vector. One estimator uses complete observations alone while the other two estimators utilize repaired data with nonstochastic and stochastic imputed values for the missing observations. Asymptotic properties of these estimators based on small disturbance asymptotic theory are derived and the impact of departure from normality of disturbances is examined.

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