FCVARestn {FCVAR} | R Documentation |
Estimate FCVAR model
Description
FCVARestn
estimates the Fractionally Cointegrated VAR model.
It is the central function in the FCVAR
package with several nested functions, each
described below. It estimates the model parameters, calculates the
standard errors and the number of free parameters, obtains the residuals
and the roots of the characteristic polynomial.
print.FCVARestn
prints the estimation results from
the output of FCVARestn
.
Usage
FCVARestn(x, k, r, opt)
Arguments
x |
A matrix of variables to be included in the system. |
k |
The number of lags in the system. |
r |
The cointegrating rank. |
opt |
An S3 object of class |
Value
An S3 object of class FCVAR_model
containing the estimation results,
including the following parameters:
startVals
Starting values used for optimization.
options
Estimation options.
like
Model log-likelihood.
coeffs
Parameter estimates.
rankJ
Rank of Jacobian for the identification condition.
fp
Number of free parameters.
SE
Standard errors.
NegInvHessian
Negative of inverse Hessian matrix.
Residuals
Model residuals.
cPolyRoots
Roots of characteristic polynomial.
printVars
Additional variables required only for printing the output of
FCVARestn
to screen.k
The number of lags in the system.
r
The cointegrating rank.
p
The number of variables in the system.
cap_T
The sample size.
opt
An S3 object of class
FCVAR_opt
that stores the chosen estimation options, generated fromFCVARoptions()
.
See Also
FCVARoptions
to set default estimation options.
FCVARestn
calls this function at the start of each estimation to verify
validity of options.
summary.FCVAR_model
prints the output of FCVARestn
to screen.
Other FCVAR estimation functions:
FCVARoptions()
,
summary.FCVAR_model()
Examples
opt <- FCVARoptions()
opt$gridSearch <- 0 # Disable grid search in optimization.
opt$dbMin <- c(0.01, 0.01) # Set lower bound for d,b.
opt$dbMax <- c(2.00, 2.00) # Set upper bound for d,b.
opt$constrained <- 0 # Impose restriction dbMax >= d >= b >= dbMin ? 1 <- yes, 0 <- no.
x <- votingJNP2014[, c("lib", "ir_can", "un_can")]
m1 <- FCVARestn(x, k = 2, r = 1, opt)
opt1 <- opt
opt1$R_psi <- matrix(c(1, 0), nrow = 1, ncol = 2)
opt1$r_psi <- 1
m1r1 <- FCVARestn(x, k = 2, r = 1, opt1)
opt1 <- opt
opt1$R_Beta <- matrix(c(1, 0, 0), nrow = 1, ncol = 3)
m1r2 <- FCVARestn(x, k = 2, r = 1, opt1)
opt1 <- opt
opt1$R_Alpha <- matrix(c(0, 1, 0), nrow = 1, ncol = 3)
m1r4 <- FCVARestn(x, k = 2, r = 1, opt1)