lineVar: Multivariate linear models: VAR and VECM

In tsDyn: Nonlinear Time Series Models with Regime Switching

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: 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	For VECM: type of deterministic regressor(s) to include in the long-term relationship. Can also be a matrix with exogeneous regressors (2OLS only).
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

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 Oct. 31, 2024, 5:08 p.m.