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“A new metric of consensus for Likert scales”

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  • Oscar Claveria

    (AQR-IREA, University of Barcelona)

Abstract

In this study we present a metric of consensus for Likert-type scales. The measure gives the level of agreement as the percentage of consensus among respondents. The proposed framework allows to design a positional indicator that gives the degree of agreement for each item and for any given number of reply options. In order to assess the performance of the proposed metric of consensus, in an iterated one-period ahead forecasting experiment we test whether the inclusion of the degree of agreement in consumers’ expectations regarding the evolution of unemployment improves out-of-sample forecast accuracy in eight European countries. We find evidence that the degree of agreement among consumers contains useful information to predict unemployment rates in most countries. The obtained results show the usefulness of consensus-based metrics to track the evolution of economic variables.

Suggested Citation

  • Oscar Claveria, 2018. "“A new metric of consensus for Likert scales”," AQR Working Papers 201810, University of Barcelona, Regional Quantitative Analysis Group, revised Oct 2018.
    • Handle: RePEc:aqr:wpaper:201810
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    File URL: http://www.ub.edu/irea/working_papers/2018/201821.pdf
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    References listed on IDEAS

    as
    1. Oscar Claveria & Enric Monte & Salvador Torra, 2017. "A new approach for the quantification of qualitative measures of economic expectations," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(6), pages 2685-2706, November.
    2. von der Gracht, Heiko A., 2012. "Consensus measurement in Delphi studies," Technological Forecasting and Social Change, Elsevier, vol. 79(8), pages 1525-1536.
    3. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    4. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    5. Kaufmann, Daniel & Scheufele, Rolf, 2017. "Business tendency surveys and macroeconomic fluctuations," International Journal of Forecasting, Elsevier, vol. 33(4), pages 878-893.
    6. Binder, Carola Conces, 2015. "Whose expectations augment the Phillips curve?," Economics Letters, Elsevier, vol. 136(C), pages 35-38.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Oscar Claveria, 2021. "On the Aggregation of Survey-Based Economic Uncertainty Indicators Between Different Agents and Across Variables," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 17(1), pages 1-26, April.
    2. Oscar Claveria, 2021. "Forecasting with Business and Consumer Survey Data," Forecasting, MDPI, vol. 3(1), pages 1-22, February.
    3. Oscar Claveria, 2020. "“Measuring and assessing economic uncertainty”," AQR Working Papers 2012003, University of Barcelona, Regional Quantitative Analysis Group, revised Jul 2020.

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    More about this item

    Keywords

    Likert scales; consensus; geometry; economic tendency surveys; consumer expectations; unemployment JEL classification: C14; C51; C52; C53; D12; E24;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling

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