Object Reference : Object View and Procedure Reference : Var
Estimate a Bayesian time-varying coefficients VAR, or BTVCVAR, model.
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.
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
Use posterior mean as the point estimate. The posterior median is used if usemean is not included in the options list.
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".
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).
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.
Do not include a constant in the exogenous regressors list.
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.
See “Bayesian Time-varying Coefficients VAR Models” for details.