lbvar: Estimate Large Bayesian VARs

In gabrielrvsc/HDeconometrics: Implementation of several econometric models in high-dimension.

Description

Estimate large Bayesian Vector Autorregressive models from Banbura et al. (2010)

Usage

lbvar(Y, p = 1, delta = 0, lambda = 0.05, xreg = NULL, ps = FALSE,
  tau = 10 * lambda)

Arguments

Y	Time-series matrix or data.frame with the VAR endogenous variables.
p	Lag order (default = 1).
delta	Numeric vector indicating the prior for the autorregressive coefficients (default = 0 for all variables). If the prior is the same for all variables the user may supply a single number. Otherwise the vector must have one element for each variable.
lambda	Constant that regulates the importance given to the priors (default = 0.05). If lambda = 0 the model ignores the data and the posterior equal the prior. For bigger lambda the model converges to the OLS estimates.
xreg	Exogenous controls.
ps	If TRUE the priors on the sum of the coefficients will be included.
tau	Controls the shrinkage in the priors on the sum of the coefficients.

Value

An object with S3 class "HDeconometricsVAR", "lbvar".

coef.by.equation	Coefficients listed by each VAR equation.
coef.by.block	Coefficients separated by blocks (intercepts, lags, exogenous).
fitted.values	In-sample fitted values.
residuals	The residuals.
Y	Supplied endogenous data.
p	VAR lag order chosen by the user.
N	Number of endogenous variables.
covmat	Residuals covariance matrix.
xreg	Exogenous controls supplied by the user.
Ts	Number of real observations, number of dummy observations and the sum of both.
delta	The delta chosen.
lambda	The lambda chosen.
call	The matched call.

References

Banbura, M., Giannone, D., & Reichlin, L. (2010). Large Bayesian vector autoregressions. Journal of Applied Econometrics, 25, 71–92.

Garcia, Medeiros and Vasconcelos (2017).

See Also

predict, HDvar, irf, fitLambda

Examples

## == This example uses the Brazilian inflation data from
#Garcia, Medeiros and Vasconcelos (2017) == ##
data("BRinf")
Y=BRinf[,1:59]# remove expectation variables
modelB=lbvar(Y,p=4)

# take a look at the coefficients
eq=coef(modelB,type="equation")
block=coef(modelB,type="block")
block$Lag1

gabrielrvsc/HDeconometrics documentation built on April 28, 2020, 7:12 a.m.