Multiple Imputation of Missing Values in Household Data with Structural Zeros
We present an approach for imputation of missing items in multivariate categor- ical data nested within households. The approach relies on a latent class model that (i) allows for household-level and individual-level variables, (ii) ensures that impossible household configurations have zero probability in the model, and (iii) can preserve multivariate distributions both within households and across households. We present a Gibbs sampler for estimating the model and generating imputations. We also describe strategies for improving the compu- tational efficiency of the model estimation. We illustrate the performance of the approach with data that mimic the variables collected in typical population censuses.