A new strategy for constructing alternative consumer confidence indexes to explain household consumption: A fuzzy DEMATEL approach
Background: Consumer Confidence Index (CCI) is a measure obtained from consumer surveys (CS) that gauges assessments and expectations of the economic environment. Common practice uses 4 of the 12 questions in CCI calculation. However, efforts to find best set of questions continue, such as the Europ...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-01-01
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| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024174786 |
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| Summary: | Background: Consumer Confidence Index (CCI) is a measure obtained from consumer surveys (CS) that gauges assessments and expectations of the economic environment. Common practice uses 4 of the 12 questions in CCI calculation. However, efforts to find best set of questions continue, such as the European Commission swapping two questions in 2019. Literature studies employ different combinations of questions; however all-alternative combinations take too much time and computational power. The questions also exhibit cause-and-effect relationships as household consumption predictors and are not statistically independent of one another. Objective: We suggest classifying the CS questions as ''Causes'' and ''Effects.'' It makes sense that inquiries in the cause group should provide a better explanation of household consumption. If this theory turns out to be correct, a smaller solution space will be able to be used to find the ideal substitute CCI. Method: A fuzzy DEMATEL (Decision-Making Trial and Evaluation Laboratory), a reliable method to present causal relationships, is used to classification. The prediction power of cause group (in terms of explaining household expenditures) is measured with the Lasso regression (Least Absolute Shrinkage and Selection Operator), which provides more interpretable regression models. This approach was applied to European Union dataset from 2007Q3 to 2021Q2. Results: The cause group included four CS questions and explained the 75% variability of the consumption expenditures. It is performed comparably to earlier studies that took into account all possible question combinations. The Türkiye case, covering data from 2007 to 2021, supported the finding of EU case, explaining 84% variation in consumption expenditures. Conclusion: These encouraging results suggest that comparable prediction power can be attained with a significant reduction in effort (in comparison to all brute force). Therefore, this approach would provide shortcut for constructing alternative CCIs to the authorities. |
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| ISSN: | 2405-8440 |