Comparative analysis of different techniques to impute expenditures into an income data set
Income and budget data seldom are measured in one and the same dataset. In order to make simulations that need both, one requires a reliable procedure to merge an income and a budget survey into one combined dataset. This workpackage contains the comparison and evaluation of five different techniques to impute expenditures into income datasets: parametric estimation of Engel curves, nonparametric estimation, both constrained and unconstrained matching using a distance function and grade correspondence. After a detailed description of the methods as well as a comparison of the main pros and cons, their effectiveness is tested upon an artificially split data file. In general, the parametric and non-parametric estimation seem to yield the best results, generating imputed values that are closest to the observed values for the budget shares.