Description Usage Arguments Value References See Also Examples
Estimate High-dimensional Vector Autorregressive models using the model chosen by the user. The model must be parametric.
1 |
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. |
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. |
Garcia, Medeiros and Vasconcelos (2017).
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ## == 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
|
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