I am trying to understand the Vector Error Correction (VEC) Model properly. I have been trying to read from several sites, went through the Chapter in Chris Brooks. But with different sources, the notations change and rather than finding my answers, I get more confused. I request guidance from the stackexchange community.
Let $Y_t, t=1,\ldots,T$, be a $p$-dimensional $I(1)$ time series. Then the VEC model is
$\Delta Y_t=\alpha\beta'Y_{t-1}+\sum\limits_{i=1}^{k}\Gamma_i\Delta Y_{t-i}+\varepsilon_t $
Now, we take $p=2$( i.e., $Y_t= \begin{bmatrix} y_{1t} & y_{2t} \\ \end{bmatrix} $) and assume $r=1$ (cointegration rank) and $k=1$ lag.
Then, $\alpha= \begin{bmatrix} \alpha_1 \\ \alpha_2 \\ \end{bmatrix} $, $\beta'= \begin{bmatrix} \beta_1 & \beta_2 \\ \end{bmatrix} $, $ \Gamma_1= \begin{bmatrix} \gamma_{11} & \gamma_{12} \\ \gamma_{21} & \gamma_{22} \\ \end{bmatrix} $
The dataset is time series with two variables: (natural) log prices of stock and futures. Based on this, I have the following query :
How large or small can the values of $\alpha$ be in ? Most estimations return values $\in(0,-1)$, but I have seen values as low as -4 (with standard error of about 7). Does this indicate mis-specification ?
Re-Edit: Removed two questions.