Impact of Departure from Normality on the Efficiency of Estimating Regression Coefficients when Some Observations are Missing

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.