A Markov chain Monte Carlo algorithm for multiple imputation in large surveys

Important empirical information on household behavior and household finances, used heavily by researchers, central banks, and for policy consulting, is obtained from surveys. However, various interdependent factors that can only be controlled to a limited extent lead to unit and item nonresponse, and missing data on certain items is a frequent source of difficulties in statistical practice. All the more, it is important to explore techniques for the imputation of large survey data. This paper presents the theoretical underpinnings of a Markov Chain Monte Carlo multiple imputation procedure and outlines important technical aspects of the application of MCMC-type algorithms to large socio-economic datasets. In an exemplary application it is found that MCMC algorithms have good convergence properties even on large datasets with complex patterns of missingness, and that the use of a rich set of covariates in the imputation models has a substantial effect on the distributions of key financial variables