IDEAS home Printed from https://ideas.repec.org/a/eee/soceps/v73y2021ics0038012119305439.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0038012119305439
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.seps.2020.100907?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. La Porta, Rafael & Lopez-de-Silanes, Florencio & Shleifer, Andrei & Vishny, Robert, 1999. "The Quality of Government," The Journal of Law, Economics, and Organization, Oxford University Press, vol. 15(1), pages 222-279, April.
    2. Marco Riani & Anthony C. Atkinson & Andrea Cerioli, 2009. "Finding an unknown number of multivariate outliers," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 447-466, April.
    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.
    7. Anthony Atkinson & Marco Riani, 2004. "The forward search and data visualisation," Computational Statistics, Springer, vol. 19(1), pages 29-54, February.
    8. Marcel P. Timmer & Erik Dietzenbacher & Bart Los & Robert Stehrer & Gaaitzen J. Vries, 2015. "An Illustrated User Guide to the World Input–Output Database: the Case of Global Automotive Production," Review of International Economics, Wiley Blackwell, vol. 23(3), pages 575-605, August.
    9. W. J. Krzanowski, 2006. "Sensitivity in Metric Scaling and Analysis of Distance," Biometrics, The International Biometric Society, vol. 62(1), pages 239-244, March.
    10. Alexander H. Foss & Marianthi Markatou & Bonnie Ray, 2019. "Distance Metrics and Clustering Methods for Mixed‐type Data," International Statistical Review, International Statistical Institute, vol. 87(1), pages 80-109, April.
    11. W. Krzanowski, 1994. "Ordination in the presence of group structure, for general multivariate data," Journal of Classification, Springer;The Classification Society, vol. 11(2), pages 195-207, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Naqvi, Asjad & Zwickl, Klara, 2017. "Fifty shades of green: Revisiting decoupling by economic sectors and air pollutants," Ecological Economics, Elsevier, vol. 133(C), pages 111-126.
    2. Aurea Grané & Rosario Romera, 2018. "On Visualizing Mixed-Type Data," Sociological Methods & Research, , vol. 47(2), pages 207-239, March.
    3. Thais NUNEZ-ROCHA & Inmaculada MARTíNEZ-ZARZOSO, 2018. "Is National Environmental Legislation Affecting Emissions?," LEO Working Papers / DR LEO 2505, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    4. Hu, Xing & Yu, Shiwei & Fang, Xu & Ovaere, Marten, 2023. "Which combinations of renewable energy policies work better? Insights from policy text synergies in China," Energy Economics, Elsevier, vol. 127(PA).
    5. Saussay, Aurélien & Zugravu-Soilita, Natalia, 2023. "International production chains and the pollution offshoring hypothesis: An empirical investigation," Resource and Energy Economics, Elsevier, vol. 73(C).
    6. Suárez-Varela, Marta & Rodríguez-Crespo, Ernesto, 2022. "Is dirty trade concentrating in more polluting countries? Evidence from Africa," Economic Analysis and Policy, Elsevier, vol. 76(C), pages 728-744.
    7. Martínez-Zarzoso, Inmaculada & Bengochea-Morancho, Aurelia & Morales-Lage, Rafael, 2019. "Does environmental policy stringency foster innovation and productivity in OECD countries?," Energy Policy, Elsevier, vol. 134(C).
    8. 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).
    9. Galeotti, Marzio & Rubashkina, Yana & Salini, Silvia & Verdolini, Elena, 2018. "Environmental policy performance and its determinants: Application of a three-level random intercept model," Energy Policy, Elsevier, vol. 114(C), pages 134-144.
    10. Sharma, Swati & Ang, James B. & Fredriksson, Per G., 2021. "Religiosity and climate change policies," Energy Economics, Elsevier, vol. 101(C).
    11. Enrico Maria de Angelis & Marina Di Giacomo & Davide Vannoni, 2019. "Climate Change and Economic Growth: The Role of Environmental Policy Stringency," Sustainability, MDPI, vol. 11(8), pages 1-15, April.
    12. Badunenko, Oleg & Galeotti, Marzio & Hunt, Lester C., 2023. "Better to grow or better to improve? Measuring environmental efficiency in OECD countries with a stochastic environmental Kuznets frontier (SEKF)," Energy Economics, Elsevier, vol. 121(C).
    13. Hartmann, Julia & Inkpen, Andrew & Ramaswamy, Kannan, 2022. "An FsQCA exploration of multiple paths to ecological innovation adoption in European transportation," Journal of World Business, Elsevier, vol. 57(5).
    14. Jackie M. L. Chan & Chih‐Sheng Hsieh, 2022. "Cross‐border networks and knowledge spillovers for foreign entry," Economic Inquiry, Western Economic Association International, vol. 60(4), pages 1730-1756, October.
    15. Xu (Susan) Tang, 2017. "Competition or Coordination: Strategic Environmental Policymaking Across OECD Countries," 2017 Papers pta721, Job Market Papers.
    16. Shouro Dasgupta, Shouro & De Cian, Enrica & Verdolini, Elena, 2016. "The Political Economy of Energy Innovation," MITP: Mitigation, Innovation and Transformation Pathways 234939, Fondazione Eni Enrico Mattei (FEEM).
    17. Barbaglia, Martina & Bianchini, Roberto & Butticè, Vincenzo & Elia, Stefano & Mariani, Marcello M., 2023. "The role of environmental sustainability in the relocation choices of MNEs: Back to the home country or welcome in a new host country?," Journal of International Management, Elsevier, vol. 29(5).
    18. Brenton R. Clarke & Andrew Grose, 2023. "A further study comparing forward search multivariate outlier methods including ATLA with an application to clustering," Statistical Papers, Springer, vol. 64(2), pages 395-420, April.
    19. Liu, Xuepeng & Mattoo, Aaditya & Wang, Zhi & Wei, Shang-Jin, 2020. "Services development and comparative advantage in manufacturing," Journal of Development Economics, Elsevier, vol. 144(C).
    20. Andreas Kyriacou & Leonel Muinelo-Gallo & Oriol Roca-Sagalés, 2015. "Construction corrupts: empirical evidence from a panel of 42 countries," Public Choice, Springer, vol. 165(1), pages 123-145, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:soceps:v:73:y:2021:i:c:s0038012119305439. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/seps .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.