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A nonparametric conditional copula-based imputation method

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  • F. Marta L. Di Lascio

    (Free University of Bozen-Bolzano, Italy)

  • Aurora Gatto

    (Free University of Bozen-Bolzano, Italy)

Abstract

Missing values in multivariate dependent variables may occur during data collection, requiring imputation methods capable of handling complex intervariable relationships. We propose a nonparametric copula-based method for imputing dependent multivariate missing data, called NPCoImp. By leveraging the conditional empirical beta copula of the missing variables given the observed ones, NPCoImp imputes data while accounting for its distributional shape, particularly radial symmetry, and adjusting the multivariate values used for imputation accordingly. NPCoImp is highly exible and can handle multivariate missing data with any type of missingness pattern. The performance of the NPCoImp has been evaluated through an extensive Monte Carlo study and compared with classical imputation methods, as well as with its direct competitor, the CoImp algorithm. Our findings indicate that NPCoImp is particularly effective in preserving microdata and dependence structure. The strong performance of the proposed method is further supported by empirical case studies in the agricultural sector. Finally, the NPCoImp algorithm has been implemented in the R package CoImp, which is available on CRAN.

Suggested Citation

  • F. Marta L. Di Lascio & Aurora Gatto, 2025. "A nonparametric conditional copula-based imputation method," BEMPS - Bozen Economics & Management Paper Series BEMPS112, Faculty of Economics and Management at the Free University of Bozen.
    • Handle: RePEc:bzn:wpaper:bemps112
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    References listed on IDEAS

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    1. Chao Chen & Jamie Twycross & Jonathan M Garibaldi, 2017. "A new accuracy measure based on bounded relative error for time series forecasting," PLOS ONE, Public Library of Science, vol. 12(3), pages 1-23, March.
    2. Brechmann, Eike Christian & Schepsmeier, Ulf, 2013. "Modeling Dependence with C- and D-Vine Copulas: The R Package CDVine," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 52(i03).
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    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • Q1 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture

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