Description Usage Arguments Value References
Estimates Time-Varying Parameters VAR model using MCMC sampler.
Y_t = Z_t β_t + ε_t; ε_t ~ N(0, H)
β_t = β_{t-1} + u_t; u_t ~ N(0, Q)
Prior parameters are estimated using OLS method on training sample.
1 2 | bayesVAR_TVP(Y, p = 1, nburn = 10000, nsim = 50000, tau = 40,
beta.algorithm = "DK", reject.explosive = FALSE)
|
Y |
Matrix or timeseries object |
p |
Integer for the lag order (default is p=1) |
nburn |
Number of MCMC draws used to initialize the sampler (defaults to 10000) |
nsim |
Number of MCMC draws excluding burn-in (defaults to 50000) |
tau |
Length of training sample used for determining prior parameters via OLS |
beta.algorithm |
Algorithm for drawing time-varying VAR parameters. Either 'DK' for Durbin and Coopman 2002, or 'CC' for Carter and Cohn 1994. Defaults to 'DK' |
reject.explosive |
Should the explosive (not stable) draws of VAR parameters be rejected? Defaults to 'FALSE'. For rationale consult Cogley and Sargent 2005. May significantly increase computtion time! |
beta |
Draws of time-varying parameters (beta_t). 4D array [t x M x M*p+1 x draw] |
H |
Draws of observation equation error term covariance matrix H. 3D array [M x M x draw] |
Q |
Draws of state equation error term covariance matrix Q. 3D array [M x M x draw] |
KoopKorobilis2010bayesVAR
CC1994bayesVAR
DK2002bayesVAR
CogleySargent2005bayesVAR
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