HDvar: Estimate High-dimensional VARs

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

Description

Estimate High-dimensional Vector Autorregressive models using the model chosen by the user. The model must be parametric.

Usage

HDvar(Y, p, fn = NULL, xreg = NULL)

Arguments

Y	Time-series matrix or data.frame with the VAR endogenous variables.
p	Lag order (default = 1).
fn	A function that receives x (independent variables) and y (dependent variable) and returns the model coefficients (default is OLS).
xreg	Exogenous controls.

Value

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

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.
T	Number of observations.
fn	The estimation function.
call	The matched call.

References

Garcia, Medeiros and Vasconcelos (2017).

See Also

predict, lbvar, irf

Examples

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

fn=function(x,y){
  model=ic.glmnet(x,y)
  coef(model)
}

modelH=HDvar(Y,p=4,fn=fn)

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

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