Impact of Departure from Normality on the Efficiency of Estimating Regression Coefficients when Some Observations are Missing
نویسنده
چکیده
This article considers a linear regression model in which some obser vations on an explanatory variable are missing and presents three least squares estimators for the regression coe cients 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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Impact of Departure from Normality on theE
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 coeecients 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 propertie...
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