In survey data, missing values are prevalent. In official economic statistics, where data are obtained through surveys, ratio imputation is often utilized to deal with missing data; however, outliers may have an influence on the imputation model. The objective of this article is to propose a new robust ratio estimator, named the TC-ratio estimator (ratio estimator with trimming based on Cook’s distance), which is robust against outliers on the vertical axis (variable y\documentclass[12pt]{minimal}
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\begin{document}$$y$$\end{document}), on the horizontal axis (variable x\documentclass[12pt]{minimal}
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\begin{document}$$x$$\end{document}), and on both axes (x\documentclass[12pt]{minimal}
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\begin{document}$$y$$\end{document}), for missing data imputation. Also, a novel way is suggested to automatically determine the number of outliers. To assess the performance of the new method, Monte Carlo simulations are conducted under 160 different data generation processes, each repeated in 10,000 simulation runs. Relative superiority of the new method is shown against the traditional robust ratio imputation methods, such as the ratio of medians, trimmed means, Winsorized means, and means by M\documentclass[12pt]{minimal}
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\begin{document}$$M$$\end{document}-estimators. The current study finds that the new method outperforms these traditional methods when outliers are present only in y\documentclass[12pt]{minimal}
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\begin{document}$$y$$\end{document}, only in x\documentclass[12pt]{minimal}
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\begin{document}$$y$$\end{document}. Furthermore, when outliers are not present, the performance of this new method is approximately equal to the non-robust method.
