Interpretability of Composite Indicators Based on Principal Components
Principal component approaches are often used in the construction of composite indicators to summarize the information of input variables. The gain of dimension reduction comes at the cost of difficulties in interpretation, inaccurate targeting, and possible conflicts with the theoretical framework...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Probability and Statistics |
| Online Access: | http://dx.doi.org/10.1155/2022/4155384 |
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| _version_ | 1832562338276835328 |
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| author | Kris Boudt Marco d’Errico Hong Anh Luu Rebecca Pietrelli |
| author_facet | Kris Boudt Marco d’Errico Hong Anh Luu Rebecca Pietrelli |
| author_sort | Kris Boudt |
| collection | DOAJ |
| description | Principal component approaches are often used in the construction of composite indicators to summarize the information of input variables. The gain of dimension reduction comes at the cost of difficulties in interpretation, inaccurate targeting, and possible conflicts with the theoretical framework when the signs in the loading are not aligned with the expected direction of impact. In this study, we propose an adjustment in the construction of principal component approaches to avoid these problems. The effectiveness of the proposed approach is illustrated in defining the Food and Agriculture Organization of the United Nations’ Resilience Capacity Index, which is used to measure household-level resilience to food insecurity. We conclude that the robustness gain of using the new method improves the reliability of the composite indicator. |
| format | Article |
| id | doaj-art-cd3f9978aad343eb9284d4fb3be44078 |
| institution | Kabale University |
| issn | 1687-9538 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Probability and Statistics |
| spelling | doaj-art-cd3f9978aad343eb9284d4fb3be440782025-02-03T01:22:54ZengWileyJournal of Probability and Statistics1687-95382022-01-01202210.1155/2022/4155384Interpretability of Composite Indicators Based on Principal ComponentsKris Boudt0Marco d’Errico1Hong Anh Luu2Rebecca Pietrelli3Department of EconomicsFood and Agriculture Organization of the United NationsDepartment of BusinessFood and Agriculture Organization of the United NationsPrincipal component approaches are often used in the construction of composite indicators to summarize the information of input variables. The gain of dimension reduction comes at the cost of difficulties in interpretation, inaccurate targeting, and possible conflicts with the theoretical framework when the signs in the loading are not aligned with the expected direction of impact. In this study, we propose an adjustment in the construction of principal component approaches to avoid these problems. The effectiveness of the proposed approach is illustrated in defining the Food and Agriculture Organization of the United Nations’ Resilience Capacity Index, which is used to measure household-level resilience to food insecurity. We conclude that the robustness gain of using the new method improves the reliability of the composite indicator.http://dx.doi.org/10.1155/2022/4155384 |
| spellingShingle | Kris Boudt Marco d’Errico Hong Anh Luu Rebecca Pietrelli Interpretability of Composite Indicators Based on Principal Components Journal of Probability and Statistics |
| title | Interpretability of Composite Indicators Based on Principal Components |
| title_full | Interpretability of Composite Indicators Based on Principal Components |
| title_fullStr | Interpretability of Composite Indicators Based on Principal Components |
| title_full_unstemmed | Interpretability of Composite Indicators Based on Principal Components |
| title_short | Interpretability of Composite Indicators Based on Principal Components |
| title_sort | interpretability of composite indicators based on principal components |
| url | http://dx.doi.org/10.1155/2022/4155384 |
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