Prediction based on conditional distributions of vine copulas

Bo Chang and Harry Joe

Computational Statistics & Data Analysis, 2019, vol. 139, issue C, 45-63

Abstract: Vine copulas are a flexible tool for multivariate non-Gaussian distributions. For data from an observational study where the explanatory variables and response variables are measured together, a proposed vine copula regression method uses regular vines and handles mixed continuous and discrete variables. This method can efficiently compute the conditional distribution of the response variable given the explanatory variables. The performance of the proposed method is evaluated on simulated data sets and a real data set. The experiments demonstrate that the vine copula regression method is superior to linear regression in making inferences with conditional heteroscedasticity.

Keywords: Regression; Nonlinear conditional mean; Conditional quantiles; Heteroscedasticity (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (15)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:139:y:2019:i:c:p:45-63

DOI: 10.1016/j.csda.2019.04.015

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