FCVARestn: Estimate FCVAR model
In FCVAR: Estimation and Inference for the Fractionally Cointegrated VAR

FCVARestn

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 `FCVAR_opt` that stores the chosen estimation options, generated from `FCVARoptions()`.

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 from FCVARoptions().

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)

FCVAR documentation built on May 5, 2022, 9:06 a.m.