lineVar: Multivariate linear models: VAR and VECM

Description Usage Arguments Details Value Author(s) See Also Examples

View source: R/lineVar.R

Description

Estimate either a VAR or a VECM.

Usage

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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: user-specified cointegrating value. If NULL, will be estimated using the estimator specified in estim

estim

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

LRinclude

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

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.

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:

Engle-Granger estimator

The Engle-Granger estimator is available

Presentation

Results are printed in a different ways, using a matrix form

lateX export

The matrix of coefficients can be exported to latex, with or without standard-values and significance stars

Two estimators are available: the Engle-Granger two-steps 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 argument beta is only for VECM, look at the specific help page for more details.

Value

Fitted model data

Author(s)

Matthieu Stigler

See Also

VECM which is just a wrapper for lineVar(..., model="VECM"). Methods predict.VAR, VARrep, regime, irf and toLatex.

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

Examples

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data(zeroyld)

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

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

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

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


###Check Johansen MLE
myVECM <- lineVar(zeroyld, lag=1, include="const", model="VECM", estim="ML")
summary(myVECM, digits=7) 
#comparing with vars package
if(require(vars)){
a<-ca.jo(zeroyld, 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)

tsDyn documentation built on June 4, 2018, 1:04 a.m.