simTVPVAR {shrinkTVPVAR} | R Documentation |
simTVPVAR
generates synthetic data from a TVP-VAR-SV model. The data is always generated as to be stationary.
This is done via a trial and error approach, where the VAR coefficients are drawn from the data generating process until
the VAR process is stationary. As such, very large models might take a long time to generate.
simTVPVAR(
N = 200,
p = 2,
m = 3,
prob_0_beta = 0.8,
prob_0_theta = 0.8,
simsig2_theta_sr = 0.2,
simsig2_beta_mean = 0.2,
intercept = TRUE,
display_progress = TRUE
)
N |
integer > 2. Indicates the length of the time series to be generated. The default value is 200. |
p |
integer > 0. Indicates the number of lags in the VAR model. The default value is 2. |
m |
integer > 1. Indicates the number of equations in the VAR model. The default value is 3. |
prob_0_beta |
numeric. Indicates the probability of a zero element in the beta_mean matrix. Can be a single value or a vector of length p. The default value is 0.8. |
prob_0_theta |
numeric. Indicates the probability of a zero element in the theta matrix. Can be a single value or a vector of length p. The default value is 0.8. |
simsig2_theta_sr |
numeric. Indicates the standard deviation of the normal distribution from which the elements of the theta matrix are drawn. The default value is 0.2. |
simsig2_beta_mean |
numeric. Indicates the standard deviation of the normal distribution from which the elements of the beta_mean matrix are drawn. The default value is 0.2. |
intercept |
logical. Indicates whether an intercept should be included in the model. The default value is TRUE. |
display_progress |
logical. Indicates whether a progress bar should be displayed. The default value is TRUE. |
The value returned is a list object containing:
data:
data frame that holds the simulated data.
true_vals:
list object containing:
Phi:
array containing the true VAR coefficients.
Sigma:
array containing the true covariance matrices.
theta_sr:
array containing the true standard deviations of the theta matrix.
beta_mean:
array containing the true means of the beta matrix.
Peter Knaus peter.knaus@wu.ac.at
# Generate a time series of length 300
res <- simTVPVAR(N = 300, m = 3, p = 3)
# Estimate a model
model <- shrinkTVPVAR(y = res$data, p = 3)