: The present article deals with the problem of estimation of parameters in a linear regression model when some data on response variable is missing and the responses are equicorrelated. The ordinary least squares and optimal homogeneous predictors are employed to find the imputed values of missing observations. Their efficiency properties are analyzed using the small disturbances asymptotic theory. The estimation of regression coefficients using these imputed values is also considered and a comparison of estimators is presented. 1 Introduction Let us consider the following linear regression model with equi-correlated disturbances: Y c = X c fi + oeffl c (1.1) where Y c is a n \Theta 1 vector of n observations on the response variable, X is a n \Theta K full column rank matrix consisting of n observations on K explanatory variables, fi is a K \Theta 1 vector of coefficients, oe is an unknown scalar and ffl c is a n \Theta 1 vector of disturbances. It is assumed that disturbances f..