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Gaussian Copula Regression in R

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  • Masarotto, Guido
  • Varin, Cristiano

Abstract

This article describes the R package gcmr for fitting Gaussian copula marginal regression models. The Gaussian copula provides a mathematically convenient framework to handle various forms of dependence in regression models arising, for example, in time series, longitudinal studies or spatial data. The package gcmr implements maximum likelihood inference for Gaussian copula marginal regression. The likelihood function is approximated with a sequential importance sampling algorithm in the discrete case. The package is designed to allow a flexible specification of the regression model and the dependence structure. Illustrations include negative binomial modeling of longitudinal count data, beta regression for time series of rates and logistic regression for spatially correlated binomial data.

Suggested Citation

  • Masarotto, Guido & Varin, Cristiano, 2017. "Gaussian Copula Regression in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 77(i08).
    • Handle: RePEc:jss:jstsof:v:077:i08
    DOI: http://hdl.handle.net/10.18637/jss.v077.i08
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    Cited by:

    1. Jiajie Kong & Robert Lund, 2023. "Seasonal count time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 44(1), pages 93-124, January.
    2. Mohamad Khoirun Najib & Sri Nurdiati & Ardhasena Sopaheluwakan, 2022. "Multivariate fire risk models using copula regression in Kalimantan, Indonesia," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 113(2), pages 1263-1283, September.
    3. Övgücan Karadağ Erdemir, 2023. "A Comparative Perspective on Multivariate Modeling of Insurance Compensation Payments with Regression-Based and Copula-Based Models," EKOIST Journal of Econometrics and Statistics, Istanbul University, Faculty of Economics, vol. 0(39), pages 161-171, December.
    4. Lennon, Hannah & Yuan, Jingsong, 2019. "Estimation of a digitised Gaussian ARMA model by Monte Carlo Expectation Maximisation," Computational Statistics & Data Analysis, Elsevier, vol. 133(C), pages 277-284.
    5. Kim, Jong-Min & Tabacu, Lucia & Jung, Hojin, 2020. "A quantile-copula approach to dependence between financial assets," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    6. Kim, Jong-Min & Lee, Namgil & Hwang, Sun Young, 2020. "A Copula Nonlinear Granger Causality," Economic Modelling, Elsevier, vol. 88(C), pages 420-430.
    7. Li Liu & Yu-Min Liu & Jong-Min Kim & Rui Zhong & Guang-Qian Ren, 2020. "Analysis of Tail Dependence between Sovereign Debt Distress and Bank Non-Performing Loans," Sustainability, MDPI, vol. 12(2), pages 1-20, January.
    8. Steve Hyun & Jimin Lee & Jong-Min Kim & Chulhee Jun, 2019. "What Coins Lead in the Cryptocurrency Market: Using Copula and Neural Networks Models," JRFM, MDPI, vol. 12(3), pages 1-14, August.

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