tsDyn (version 0.9-44)

VECM: Estimation of Vector error correction model (VECM)


Estimate either a VECM by Engle-Granger or Johansen (MLE) method.


VECM(data, lag, r = 1, include = c("const", "trend", "none", "both"),
  beta = NULL, estim = c("2OLS", "ML"), LRinclude = c("none", "const",
  "trend", "both"), exogen = NULL)



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


Number of lags (in the VECM representation, see Details)


Number of cointegrating relationships


Type of deterministic regressors to include


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


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


Type of deterministic regressors to include in the long-term relationship. Can also be a matrix with exogeneous regressors (2OLS only).


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


An object of class VECM (and higher classes VAR and nlVar) with methods:

Usual methods

Print, summary, plot, residuals, fitted, vcov

Fit criteria

AIC, BIC, MAPE, mse, logLik (latter only for models estimated with MLE)


Predict and predict_rolling

VAR/VECM methods

Impulse response function (irf) and forecast error variance decomposition (fevd)




This function is just a wrapper for the lineVar, with model="VECM".

More comprehensive functions for VECM are in package vars. A few differences appear in the VECM estimation:

Engle-Granger estimator

The Engle-Granger estimator is available


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


The predict method contains a newdata argument allowing to compute rolling forecasts.

Two estimators are available: the Engle-Granger two step approach (2OLS) or the Johansen (ML). For the 2OLS, deterministics 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).

Note that the lag specification corresponds to the lags in the VECM representation, not in the VAR (as is done in package vars or software GRETL). Basically, a VAR with 2 lags corresponds here to a VECM with 1 lag. Lag 0 in the VECM is not allowed.

#'The arg beta allows to specify constrained cointegrating values, leading to \(ECT= \beta^{'}X_{t-1}\). It should be specified as a \(K \times r\) matrix. In case of \(r=1\), can also be specified as a vector. Note that the vector should be normalised, with the first value to 1, and the next values showing the opposite sign in the long-run relationship \(- \beta\). In case the vector has \(K-1\) values, this is what lineVar is doing, setting \((1, - \beta)\). Note finally one should provide values for all the coefficients (eventually except for special case of r=1 and k-1), if you want to provide only part of the parameters, and let the others be estimated, look at the functions in package urca.

See Also

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


Run this code

#Fit a VECM with Engle-Granger 2OLS estimator:
vecm.eg<-VECM(zeroyld, lag=2)

#Fit a VECM with Johansen MLE estimator:
vecm.jo<-VECM(zeroyld, lag=2, estim="ML")

#compare results with package vars:
if(require(vars)) {
 #check long coint values
   all.equal(VECM(finland, lag=2, estim="ML", r=2)$model.specific$beta,
             cajorls(ca.jo(finland, K=3, spec="transitory"), r=2)  $beta, check.attributes=FALSE)
# check OLS parameters
  all.equal(t(coefficients(VECM(finland, lag=2, estim="ML", r=2))),
    coefficients(cajorls(ca.jo(finland, K=3, spec="transitory"), r=2)$rlm), check.attributes=FALSE)


##export to Latex
toLatex(summary(vecm.eg), parenthese="Pvalue")
# }

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