simul_fun_generalized_2d: A Special Case of simulation_generalized in 2 Dimensions

Description

A Special Case of simulation_generalized in 2 Dimensions

Usage

simul_fun_generalized_2d(
  nsim,
  n_train,
  n_test,
  copula,
  init_params,
  fn,
  u1,
  u2,
  z1_train,
  z2_train,
  X_t,
  y1_test,
  y2_test,
  BSTS_1,
  BSTS_2
)

Value

A list containing:

theta_simulated: Simulated copula parameters across replications.
y1_simulated: Simulated values for the first response variable.
y2_simulated: Simulated values for the second response variable.
MSE: Mean squared error for each simulation run.
optim_results: Results from the optimization process.

Arguments

nsim: Integer, number of simulation replications.
n_train: Integer, number of training observations.
n_test: Integer, number of test observations.
copula: Character, specifying the copula type: "Clayton", "Frank", "Gumbel", "Joe", or "Gaussian".
init_params: Numeric vector, initial parameter values for optimization.
fn: Function, log-likelihood function for parameter estimation.
u1: Numeric vector (n_train), first pseudo-observation for the copula.
u2: Numeric vector (n_train), second pseudo-observation for the copula.
z1_train: Numeric matrix (n_train x M), observed data for the first margin.
z2_train: Numeric matrix (n_train x M), observed data for the second margin.
X_t: Numeric matrix (n_train x M), risk factors for the dynamic copula parameter.
y1_test: Numeric vector (n_test), true future values for the first response variable.
y2_test: Numeric vector (n_test), true future values for the second response variable.
BSTS_1: Fitted BSTS model for the first response variable.
BSTS_2: Fitted BSTS model for the second response variable.