The Log-Likelihood is computed in two dfferent ways,
depending on whether the VECM was estimated with
ML (Johansen) or 2OLS (Engle and Granger). When the model is estimated with ML, the LL is computed
as in Hamilton (1994) 20.2.10 (p. 637):
$$LL = -(TK/2) \log(2\pi) - (TK/2) -(T/2)
\log|\hat\Sigma_{UU}| - (T/2) \sum_{i=1}^{r} \log
(1-\hat\lambda_{i})$$ Where $\Sigma_{UU}$ is the
variance matrix of residuals from the first auxiliary
regression, i.e. regressing $\Delta y_t$ on a
constant and lags, $\Delta y_{t-1}, \ldots, \Delta
y_{t-p}$. $\lambda_{i}$ are the eigenvalues from the
$\Sigma_{VV}^{-1}\Sigma_{VU}\Sigma_{UU}^{-1}\Sigma_{UV}$,
see 20.2.9 in Hamilton (1994).
When the model is estimated with 2OLS, the LL is computed
as: $$LL = \log|\Sigma|$$
Where $\Sigma$ is the variance matrix of residuals
from the the VECM model. There is hence no correspondance
between the LL from the VECM computed with 2OLS or ML.