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Introducing localgauss, an R Package for Estimating and Visualizing Local Gaussian Correlation

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  • Berentsen, Geir Drage
  • Kleppe, Tore Selland
  • Tjøstheim, Dag Bjarne

Abstract

Quantifying non-linear dependence structures between two random variables is a challenging task. There exist several bona-fide dependence measures able to capture the strength of the non-linear association, but they typically give little information about how the variables are associated. This problem has been recognized by several authors and has given rise to the concept of local measures of dependence. A local measure of dependence is able to capture the “local” dependence structure in a particular region. The idea is that the global dependence structure is better described by a portfolio of local measures of dependence computed in different regions than a one-number measure of dependence. This paper introduces the R package localgauss which estimates and visualizes a measure of local dependence called local Gaussian correlation. The package provides a function for estimation, a function for local independence testing and corresponding functions for visualization purposes, which are all demonstrated with examples.

Suggested Citation

  • Berentsen, Geir Drage & Kleppe, Tore Selland & Tjøstheim, Dag Bjarne, 2014. "Introducing localgauss, an R Package for Estimating and Visualizing Local Gaussian Correlation," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 56(i12).
    • Handle: RePEc:jss:jstsof:v:056:i12
    DOI: http://hdl.handle.net/10.18637/jss.v056.i12
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    References listed on IDEAS

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    1. Rosenblatt, Murray & Wahlen, Bruce E., 1992. "A nonparametric measure of independence under a hypothesis of independent components," Statistics & Probability Letters, Elsevier, vol. 15(3), pages 245-252, October.
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    1. repec:rim:rimwps:23-09 is not listed on IDEAS
    2. Dimitrios Dimitriou & Dimitris Kenourgios & Theodore Simos & Alexandros Tsioutsios, 2025. "The implications of non‐synchronous trading in G‐7 financial markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 30(1), pages 689-709, January.
    3. Bampinas, Georgios & Panagiotidis, Theodore & Politsidis, Panagiotis N., 2023. "Sovereign bond and CDS market contagion: A story from the Eurozone crisis," Journal of International Money and Finance, Elsevier, vol. 137(C).
    4. Georgios Bampinas & Theodore Panagiotidis, 2017. "Oil and stock markets before and after financial crises: A local Gaussian correlation approach," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 37(12), pages 1179-1204, December.
    5. Li, Dongxin & Zhang, Feipeng & Yuan, Di & Cai, Yuan, 2024. "Does COVID-19 impact the dependence between oil and stock markets? Evidence from RCEP countries," International Review of Economics & Finance, Elsevier, vol. 89(PA), pages 909-939.
    6. Elie Bouri & Rangan Gupta & Shixuan Wang, 2019. "Contagion between Stock and Real Estate Markets: International Evidence from a Local Gaussian Correlation Approach," Working Papers 201917, University of Pretoria, Department of Economics.
    7. Otneim, Håkon & Jullum, Martin & Tjøstheim, Dag, 2020. "Pairwise local Fisher and naive Bayes: Improving two standard discriminants," Journal of Econometrics, Elsevier, vol. 216(1), pages 284-304.
    8. Elie Bouri & Rangan Gupta & Shixuan Wang, 2022. "Nonlinear contagion between stock and real estate markets: International evidence from a local Gaussian correlation approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 2089-2109, April.
    9. Lars Arne Jordanger & Dag Tjøstheim, 2023. "Local Gaussian Cross-Spectrum Analysis," Econometrics, MDPI, vol. 11(2), pages 1-27, April.

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