@article {10.34196/ijm.00226,
article_type = {journal},
title = {Comparative analysis of different techniques to impute expenditures into an income data set},
author = {Decoster, André and Rock, Bram De and Swerdt, Kris De and Loughrey, Jason and O’Donoghue, Cathal and Verwerft, Dirk},
volume = 13,
number = 3,
year = 2020,
month = {dec},
pub_date = {2020-12-31},
pages = {70-94},
citation = {IJM 2020;13(3):70-94},
doi = {10.34196/ijm.00226},
url = {https://doi.org/10.34196/ijm.00226},
abstract = {Income and budget data seldom are measured in 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 paper 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.},
keywords = {microsimulation, statistical matching},
journal = {IJM},
issn = {1747-5864},
publisher = {International Journal of Microsimulation},
}