tsDyn (version 0.9-32)

lineVar: Multivariate linear models: VAR and VECM

Description

Estimate either a VAR or a VECM.

Usage

lineVar(data, lag, r = 1,
    include = c("const", "trend", "none", "both"),
    model = c("VAR", "VECM"),
    I = c("level", "diff", "ADF"), beta = NULL,
    estim = c("2OLS", "ML"),
    LRinclude = c("none", "const", "trend", "both"),
    exogen = NULL)

Arguments

data

multivariate time series (first row being first=oldest value)

lag

Number of lags to include in each regime

r

Number of cointegrating relationships

include

Type of deterministic regressors to include

model

Model to estimate. Either a VAR or a VECM

I

For VAR only: whether in the VAR the variables are to be taken in levels (original series) or in difference, or similarly to the univariate ADF case.

beta

for VECM only: cointegrating value. If null, will be estimated

LRinclude

Possibility to include in the long-run relationship and the ECT trend, constant... Can also be a matrix with exogeneous regressors

estim

Type of estimator for the VECM: '2OLS' for the two-step approach or 'ML' for Johansen MLE

exogen

Inclusion of exogenous variables (first row being first=oldest value). Is either of same size than data (then automatically cut) or than end-sample.

Value

• Fitted model data

Details

This function provides basic functionalities for VAR and VECM models. More comprehensive functions are in package vars. A few differences appear in the VECM estimation: [object Object],[object Object],[object Object]

Two estimators are available: the Engle-Granger two step approach (2OLS) or the Johansen (ML). For the 2OLS, deterministic regressors (or external variables if LRinclude is of class numeric) can be added for the estimation of the cointegrating value and for the ECT. This is only working when the beta value is not pre-specified.

The arg beta is the cointegrating value, the cointegrating vector will be taken as: (1, -beta).

See Also

VECM which is just a wrapper for lineVar(...,model="VECM")

TVAR and TVECM for the corresponding threshold models. linear for the univariate AR model.

Examples

data(zeroyld)
data<-zeroyld

#Fit a VAR
VAR<-lineVar(data, lag=1)
VAR
summary(VAR)

#compare results with package vars:
if(require(vars)) {
	a<-VAR(data, p=1)
	coef_vars <- t(sapply(coef(a), function(x) x[c(3,1,2),1]))
	all.equal(coef(VAR),coef_vars, check=FALSE)
}

###VECM
VECM.EG<-lineVar(data, lag=2, model="VECM")
VECM.EG
summary(VECM.EG)

VECM.ML<-lineVar(data, lag=2, model="VECM", estim="ML")
VECM.ML
summary(VECM.ML)


###Check Johansen MLE
myVECM<-lineVar(data, lag=1, include="const", model="VECM", estim="ML")
summary(myVECM, digits=7)
#comparing with vars package
if(require(vars)){
	a<-ca.jo(data, spec="trans")
	summary(a)
	#same answer also!
}

##export to Latex
toLatex(VECM.EG)
toLatex(summary(VECM.EG))
options("show.signif.stars"=FALSE)
toLatex(summary(VECM.EG), parenthese="Pvalue")
options("show.signif.stars"=TRUE)