Book Description

The second essay, "Imputations: Benefits, Risks and a Method for Missing Data", examines methods to deal with missing variables and missing observations. While the conditions under which missing data does not lead to bias as well as the conditions for missing data methods to yield consistent estimates are well understood, they often do not hold in practice. In order to provide guidance on whether to omit the missing data or to apply a method for missing data, the paper first examines conditions under which these methods can improve estimates and then discusses biases from frequent violations of their assumptions. I then discuss advantages and problems of common missing data methods. Two important problems are that most methods work well for some models, but poorly for others and that researchers often do not have enough information to use imputed observations in common data sets appropriately. To address these problems, I develop a method based on the conditional density and apply it to common problems to show that it works well in practice.