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A spatially-weighted AMH copula-based dissimilarity measure to cluster variables in panel data

Author

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  • F. Marta L. Di Lascio

    (Free University of Bozen-Bolzano, Italy)

  • Andrea Menapace

    (Free University of Bozen-Bolzano, Italy)

  • Roberta Pappadà

    (University of Trieste, Italy)

Abstract

Investigating thermal energy demand is crucial for the development of sustainable cities and efficient use of renewable sources. Despite the advances made in this field, the analysis of energy data provided by smart grids is currently a demanding challenge due to their complex multivariate structure and high-dimensionality. In this paper, we develop a clustering methodology based on a novel copula-based dissimilarity measure suitable for analyzing a high temporal resolution panel data for district heating demand. Inspired by the characteristics of this data, we explore the usefulness of the Ali-Mikhail-Haq copula in defining a new dissimilarity measure to cluster variables in a hierarchical framework. We show that our proposal is particularly sensitive to small dissimilarities based on tiny differences in the dependence level. Therefore, the measure we introduce is able to better distinguish between objects with low dissimilarity than classic rank-based dissimilarity measures. Moreover, our proposal considers a weighted version of the copula-based dissimilarity that embeds the spatial location of the involved data objects. We investigate the proposed measure through Monte Carlo studies and compare it with the corresponding Kendall’s correlation-based dissimilarity measure. Finally, the application to real data concerning the Italian city Bozen- Bolzano makes it possible to find clusters of buildings homogeneous with respect to their main characteristics, such as energy efficiency and heating surface, to support the design, expansion and management of district heating systems.

Suggested Citation

  • F. Marta L. Di Lascio & Andrea Menapace & Roberta Pappadà, 2021. "A spatially-weighted AMH copula-based dissimilarity measure to cluster variables in panel data," BEMPS - Bozen Economics & Management Paper Series BEMPS89, Faculty of Economics and Management at the Free University of Bozen.
    • Handle: RePEc:bzn:wpaper:bemps89
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    References listed on IDEAS

    as
    1. Giovanni De Luca & Paola Zuccolotto, 2011. "A tail dependence-based dissimilarity measure for financial time series clustering," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 5(4), pages 323-340, December.
    2. F. Marta L. Di Lascio & Andrea Menapace & Maurizio Righetti, 2020. "Joint and conditional dependence modelling of peak district heating demand and outdoor temperature: a copula-based approach," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(2), pages 373-395, June.
    3. Giovanni De Luca & Paola Zuccolotto, 2021. "Regime dependent interconnectedness among fuzzy clusters of financial time series," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 15(2), pages 315-336, June.
    4. Giovanni De Luca & Paola Zuccolotto, 2017. "Dynamic tail dependence clustering of financial time series," Statistical Papers, Springer, vol. 58(3), pages 641-657, September.
    5. F. Marta L. Di Lascio & Andrea Menapace & Maurizio Righetti, 2020. "Analysing the relationship between district heating demand and weather conditions through conditional mixture copula," BEMPS - Bozen Economics & Management Paper Series BEMPS68, Faculty of Economics and Management at the Free University of Bozen.
    6. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(2), pages 277-297.
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    More about this item

    Keywords

    Ali-Mikhail-Haq copula; Cluster analysis; Dissimilarity measure; District heating demand; Panel data; Spatial weight.;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis

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