A nonparametric conditional copula-based imputation method

F. Marta L. Di Lascio () and Aurora Gatto ()
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F. Marta L. Di Lascio: Free University of Bozen-Bolzano, Italy
Aurora Gatto: Free University of Bozen-Bolzano, Italy

No BEMPS112, BEMPS - Bozen Economics & Management Paper Series from Faculty of Economics and Management at the Free University of Bozen

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.

Keywords: Asymmetry; Conditional copula; Empirical copula; Imputation methods; Multivariate missing data; NPCoImp. (search for similar items in EconPapers)
JEL-codes: C1 C14 C63 Q1 (search for similar items in EconPapers)
Pages: [35 pages]
Date: 2025-05
New Economics Papers: this item is included in nep-ecm
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