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Robust multivariate analysis for mixed-type data: Novel algorithm and its practical application in socio-economic research

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  • Grané, Aurea
  • Salini, Silvia
  • Verdolini, Elena

Abstract

We propose a novel method and algorithm for the analysis and clustering of mixed-type data using a hierarchical approach based on Forward Search. In our procedure, the identification of groups is based on the identification of similar trajectories and then linked to very intuitive two-dimensional maps. The proposed algorithm can use different measures for the calculation of distance in the case of mixed-type data, such as Gower’s metric and Related metric scaling. A key feature of our algorithm is its ability to discard redundant information from a given set of variables. The practical usefulness of the algorithm is illustrated through two applications of high relevance for empirical economic research. The first one focuses on comparing different indicators of environmental policy stringency in different countries. The second one applies our procedure to identify clusters of countries based on information regarding their institutional characteristics.

Suggested Citation

  • Grané, Aurea & Salini, Silvia & Verdolini, Elena, 2021. "Robust multivariate analysis for mixed-type data: Novel algorithm and its practical application in socio-economic research," Socio-Economic Planning Sciences, Elsevier, vol. 73(C).
    • Handle: RePEc:eee:soceps:v:73:y:2021:i:c:s0038012119305439
    DOI: 10.1016/j.seps.2020.100907
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    References listed on IDEAS

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    3. Galeotti, Marzio & Salini, Silvia & Verdolini, Elena, 2020. "Measuring environmental policy stringency: Approaches, validity, and impact on environmental innovation and energy efficiency," Energy Policy, Elsevier, vol. 136(C).
    4. Silvia Salini & Andrea Cerioli & Fabrizio Laurini & Marco Riani, 2016. "Reliable Robust Regression Diagnostics," International Statistical Review, International Statistical Institute, vol. 84(1), pages 99-127, April.
    5. Claire Brunel & Arik Levinson, 2013. "Measuring Environmental Regulatory Stringency," Working Papers gueconwpa~13-13-02, Georgetown University, Department of Economics.
    6. Enrico Botta & Tomasz Koźluk, 2014. "Measuring Environmental Policy Stringency in OECD Countries: A Composite Index Approach," OECD Economics Department Working Papers 1177, OECD Publishing.
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    Cited by:

    1. Peiró-Signes, Ángel & Segarra-Oña, Marival & Trull-Domínguez, Óscar & Sánchez-Planelles, Joaquín, 2022. "Exposing the ideal combination of endogenous–exogenous drivers for companies’ ecoinnovative orientation: Results from machine-learning methods," Socio-Economic Planning Sciences, Elsevier, vol. 79(C).
    2. Galati, Antonino & Coticchio, Alessandro & Peiró-Signes, Ángel, 2023. "Identifying the factors affecting citizens' willingness to participate in urban forest governance: Evidence from the municipality of Palermo, Italy," Forest Policy and Economics, Elsevier, vol. 155(C).
    3. Aurea Grané & Alpha A. Sow-Barry, 2021. "Visualizing Profiles of Large Datasets of Weighted and Mixed Data," Mathematics, MDPI, vol. 9(8), pages 1-20, April.
    4. Amparo Baíllo & Aurea Grané, 2021. "Subsampling and Aggregation: A Solution to the Scalability Problem in Distance-Based Prediction for Mixed-Type Data," Mathematics, MDPI, vol. 9(18), pages 1-17, September.

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