post_normal_covar_tvp {bvartools} | R Documentation |
Posterior Simulation of Error Covariance Coefficients
Description
Produces posterior draws of time varying error covariance coefficients.
Usage
post_normal_covar_tvp(y, u_omega_i, v_sigma_i, psi_init)
Arguments
y |
a |
u_omega_i |
matrix of error variances of the measurement equation.
Either a |
v_sigma_i |
matrix of error variances of the state equation.
Either an |
psi_init |
a vector of inital values of the state equation. |
Details
For the multivariate model with
the function produces a draw of the lower triangular part of
similar as in
Primiceri (2005), i.e., using
where
and denotes the first to
th elements of the vector
.
The algorithm of Chan and Jeliazkov (2009) is used to obtain time varying coefficients.
Value
A matrix.
References
Chan, J., & Jeliazkov, I. (2009). Efficient simulation and integrated likelihood estimation in state space models. International Journal of Mathematical Modelling and Numerical Optimisation, 1(1/2), 101–120. doi:10.1504/IJMMNO.2009.030090
Primiceri, G. E. (2005). Time varying structural vector autoregressions and monetary policy. The Review of Economic Studies, 72(3), 821–852. doi:10.1111/j.1467-937X.2005.00353.x
Examples
# Load example data
data("e1")
y <- log(t(e1))
# Generate artificial draws of other matrices
u_omega_i <- diag(1, 3)
v_sigma_i <- diag(1000, 3)
psi_init <- matrix(0, 3)
# Obtain posterior draw
post_normal_covar_tvp(y, u_omega_i, v_sigma_i, psi_init)