hofa.DGP1: Generate panel data from [H]igher-[O]rder multi-cumulant...

In GuanglinHuang/hofa: higher-order multi-cumulant factor analysis

Generate panel data from [H]igher-[O]rder multi-cumulant [F]actor [A]nalysis of DGP1

Description

Generate panel data from [H]igher-[O]rder multi-cumulant [F]actor [A]nalysis of DGP1

Usage

hofa.DGP1(n, t, k, par_f, par_e, alpha, rho_f, rho_e = 0.2, ...)

Arguments

n	An integer, the number of variables.
t	An integer, the number of observations.
k	An integer, the number of factors.
par_f	A list of four elements, the parameters of factor distribution, the first list is sig_f = (sig_f1,sig_f2,...), the second list is eta_f = (eta_f1,eta_f2,...), the third list is p_f = (p_f1,p_f2,...), the fourth list is q_f = (q_f1,q_f2,...).
par_e	A list of four elements, the parameters of error distribution, par_e = list(sig_e,eta_e,p_e,q_e).
alpha	The eigenvalue rate of error terms.
rho_f	A k x 1 vector, the auto-regressive parameters of factors.
rho_e	The auto-regressive parameters of errors.
...	Any other parameters.

Value

A T x N matrix, generated by DGP1 of hofa.

Examples

n = 100
t = 200
k = 2
par_f = list(rep(1,k),rep(0.8,k),rep(1,k),rep(Inf,k))
par_e = list(1,0,2,Inf)
rho_f = c(0.5,0.2)
alpha = 0
hofa.DGP1(n,t,k,par_f,par_e,alpha,rho_f,rho_e = 0.2)

GuanglinHuang/hofa documentation built on Sept. 3, 2023, 7:01 a.m.