Missing value imputation via copula and transformation methods, with applications to financial and economic data

摘要

We present new, tractable methods to impute missing values based on conditional probability density functions that we estimate via copula and mixture models. Our methods exploit known analytical results concerning conditional distributions for the Arellano-Valle and Bolfarine's generalised t-distribution and fast, accurate quadrature methods. We also benchmark our approach on three financial/economic datasets (two of which are publicly available) and show that our methods outperform benchmark approaches on these data.