Estimate a Bayesian time-varying coefficients VAR, or BTVCVAR, model.
Syntax
var_name.btvcvar(options) lag_pairs endog_list [@ exog_list]
btvcvar estimates a Bayesian time-varying coefficients VAR. The order of the VAR is specified using a lag pair, followed by a list of series or groups for endogenous variables. Exogenous variables can be included using the @-sign followed by a list of series or groups. A constant is automatically added to the list of exogenous variables; to estimate a specification without a constant, use the noconst option.
Options
Prior hyper-parameters
T0 = int (default = 0) | Set prior sample size  . A prior sample is not used if T0 is set to 0. To use a prior sample, T0 must be set to an integer larger than the number of coefficients per equation in a standard VAR version of the model. |
tau0 = num (default = 5.0) | Set prior scaling parameter for the initial state  . |
tau1 = num (default = 1.0) | Set prior scaling parameter for the observation covariance  . |
nu1 = num (default = 5.0) | Set prior dof parameter for the observation covariance  . |
tau2 = num (default = 0.01) | Set prior scaling parameter for the process covariance  . |
nu2 = num (default = 5.0) | Set prior dof parameter for the process covariance  . |
Display options
usemean | Use posterior mean as the point estimate. The posterior median is used if usemean is not included in the options list. |
showci | Show credibility intervals (bands). |
cilevels = arg (default = "0.95") | Set credibility levels. For multiple levels, enter a space-delimited list of values surrounded by quotation marks, e.g., "0.3 0.5 0.8". |
uselines | Use lines instead of shading for credibility intervals. |
MCMC options
burn = int (default = 5000) | Set burn-in size. |
size = int (default = 5000) | Set posterior sample size. |
thin = int (default = 1) | Set thinning size. A thinning size of  indicates that every  -th draw after the burn-in period is stored. |
nsub = int | Set the number of subchains. |
seed = int | Set the random seed. EViews will generate a seed if one is not specified. |
rng = arg (default = “kn” or method set via rndseed) | Set random number generator type. Available types are: improved Knuth generator (“kn”), improved Mersenne Twister (“mt”), Knuth’s (1997) lagged Fibonacci generator used in EViews 4 (“kn4”), L’Ecuyer’s (1999) combined multiple recursive generator (“le”), Matsumoto and Nishimura’s (1998) Mersenne Twister used in EViews 4 (“mt4”). |
Other options
smoother = arg (default = "CFA") | Set simulation smoothing method. Available methods are "CFA" (Cholesky factor algorithm), "KFS" (Kalman filter and smoother), and "MMP" (McCausland, Miller, & Pelletier, 2011). |
stable | Use the method of Cogley & Sargent for obtaining stable draws. |
maxattempts = int (default = 100) | Set the maximum number of attempts for the sampler to draw stable VAR coefficients for all dates in the data sample. |
noconst | Do not include a constant in the exogenous regressors list. |
Examples
To declare and estimate a BTVCVAR named MYVAR with endogenous variables DGP and UNEMP, a constant, the first lag, and a prior sample of size 40, run
var myvar.btvcvar(t0=40) 1 1 gdp unemp
in the command window. Running the command
var myvar.btvcvar(t0=40, showci, cilevels="0.3 0.5") 1 1 gdp unemp
will also display shaded 30% and 50% credibility bands. For reproducible results, also set the number of subchains (nsub), the random seed (seed), and the random number generator type (rng):
var myvar.btvcvar(t0=40, nsub=12, seed=342458900, rng=kn, showci, cilevels="0.3 0.5") 1 1 gdp unemp
Command capture will always show the nsub, seed, and rng options.
Cross-references