factorcopula - r package for high dimensional specification, simulation and estimation of factor copula models
This project is archived and no longer actively maintained.
- simulation from user specified factor copula models (e.g. factors and error terms from the skew t, normal, t distribution)
- estimation of high dimensional dependence structures using the simulated methods of moments
- unrestricted, equi-dependence and block-equi-dependence model specification
- implementation of a structural break test
- estimation of asymptotic variance and confidence intervalls
- implementation of the J test for overidentifying restrictions
- implementation of faster C++ code with Rcpp
Please cite the following paper when using any parts of this library in your research:
@phdthesis{bonart_malte_2020_3749627,
author = {Bonart, Malte},
title = {{Testing for Structural Breaks in Factor Copula
Models - Implementation and Application in Social
Media Topic Analysis}},
school = {University of Cologne},
year = 2020,
month = apr,
note = {{Submitted for the Master Examination in Economics
at the Faculty of Management, Economics and Social
Sciences of the University of Cologne in June
2018.}},
doi = {10.5281/zenodo.3749627},
url = {https://doi.org/10.5281/zenodo.3749627}
}install.packages("devtools")
devtools::install_github("bonartm/factorcopula")library(factorcopula)
help(package = "factorcopula")
# define a one factor skew-t copula
t <- 1500
k <- c(1, 1) # all variables in the same groups for an equidependence model
beta <- config_beta(k = k, Z = 1)
Z <- config_factor(rst = list(nu = 1/0.25, lambda = lambda), par = c("lambda"))
eps <- config_error(rt = list(df = 1/0.25))
# define the vector of true parameters
theta0 <- c(beta1 = 1.5, lambda = -0.8)
# generate the copula function and simulate values from the copua model
cop <- fc_create(Z, eps, beta)
U <- cop(theta0, t)
# use some marginal distributions (here normal distribution) to simulate some Y values
Y <- qnorm(U)
# define boundaries for optimzation
lower <- c(beta1 = 0, lambda = -0.9)
upper <- c(beta1 = 5, lambda = 0.9)
# fit the copula
m <- fc_fit(Y, Z, eps, beta, lower, upper, S = 20000, se = TRUE)
m$theta.second.stage
m$Q
m$message
# plot observed and simulated values
plot(Y, pch = 20)
points(qnorm(cop(m$theta.second.stage, 2000)), col = "red", pch = 20)
# confidence intervalls and p-values
round(m$ci, 4)
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