Efficiency gains due to using missing data procedures in regression models

Abstract

The problem of missing observations in regression models is often solved by using imputed values to complete the sample. As an alternative for static models, it has been suggested to limit the analysis to the periods or units for which all relevant variables are observed. The choice of an imputation procedure affects the asymptotic efficiency of the method used to subsequently estimate the parameters of the model. In this note, we show that the relative asymptotic efficiency of three estimators designed to handle incomplete samples depends on parameters that have a straightforward statistical interpretation. In terms of a gain of asymptotic efficiency, the use of these estimators is equivalent to the observation of a percentage of the values which are actually missing. This percentage depends on three R2-measures only, which can be straightforwardly computed in applied work. Therefore it should be easy in practice to check whether it is worthwhile to use a more elaborate estimator.