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i did johansen (x1,x2,x3,x4) and Trace test indicated 1 cointegrating eqn(s) at the 0.05 level What should i follow now? … I saw this thread- VAR or VECM for a mix of stationary and nonstationary variables , but couldnt understand what should i do in eviews …

I test for Cointegration with all original variables using Johansen's test, the output is "Trace test indicates 1 cointegration eqn(s) at the 0.05 level". How i should interpret it? … Should I use VAR or VECM if the goal is forecast all variables? And how can i build these models? Can you please give any good research? …

I am considering two time series and I would like to to a VAR regression between them. The ADF test rejected stationarity in only one of them, so the time series would be I(0) and I(1). … Should I consider doing a VAR of the I(0) in levels and the differences in the I(1)? Should I still consider a VECM? …

an I(1) dependent variable (y) and an I(0) explanatory variable (x), a model of VAR cannot be selected because y is non-stationary. … (*I have seen that I can add an explanatory variable which is non stationary I(1) in order to compute a VECM but this not possible in my case). …

As far as I know, one should consider using the VECM if the multivariate time-series of interest consists of cointegrated I(1) processes. … However, the study did not use the VECM, but estimated a VAR model. One more question: consider two cointegrated I(1) processes. …

When I run a regression, all variables are I(1), the optimal lag according to SIC is one, and means I should do VECM (1-1=0) the coefficient of the error correction term (ECT) is negative but not significant … My question is: can I do VAR instead of VECM, Should I do VAR of differencs: means in E-views: D(dependent Variable) D(Variable1) D(Variable2) D(Variable3)..and so on. …

Is there ever a reason to use a first differenced VAR over a VECM when all your variables are I(1) and co integration exists? … $$\Delta \text{CPI}_t^p=\Sigma_{i=0}^4\eta_i \Delta\text{MW}_{t-i}+\Delta \alpha_1\text{CPI}_{t-1}^{p}+\alpha_2\Delta\text{UR}_t^p+\mu^m+\mu^y+\mu^p+\mathcal{E}_t$$ Is my understanding of the model correct …

For the VECM model, I have selected the lag ($p=1$) using: VAR with variables at level, statistical criterion of AIC, no evidence of autocorrelation or heteroscedasticity in the residuals of VAR, … lag of VECM equal lag of VAR in level minus 1, VECM(0). …

Having taken the first difference, they do all become stationary at I(1). I was wondering, do I perform the Johansen cointegration test on the I(0) variables or the I(1) variables. … 2) If I have cointegration present, and my I(0) variables are non-stationary, I should switch to a VECM model? ) …

Suppose a VAR(p) model containing $s$ endogenous variables such that $Y_{t} = (y_{1t}, \ \ldots, y_{st})$. It was verified that, for all $i \in \{1, \ \ldots, s\}$, $y_{it} \sim I(1)$. … 0)$. …

$\eta_t \sim I(0)$. … Prove that $B'C=0$ and $CA=0$ Comments: I don't understand why $B'y_t$ is stationary, wouldn't it be more easier to do the following thing: \begin{aligned} y_t-y_{t-1} &= c-AB'y_{t-1}+\epsilon _t \\ y_t …

I am estimating the following VAR model: \begin{equation*} x_t = k + A_1 x_{t-1} + A_2 x_{t-2} + \dots + A_p x_{t-p} + \epsilon_t, \end{equation*} where $x_t$ is a vector of variables and notation is … I have three variables in $x_t$: Two $I(1)$ processes and one $I(0)$ process. The Johansen cointegration test yields rank 1, such that there is one cointegrating relationship. …

All my data are in natural logarithms. 1) All my variables are I(1) except an exogenous variable that is I(0), is a stock that is growing but at a diminishing rate. … Do I include this variable as an exogenous variable in the VAR (and can I include it in Stata in the VECM for cointegration?) 2) How do I deal with the changing trend in the graph below? …

My idea is to estimate the VECM and (supposing I can get a good fit for the model by some criterion) reformulate the VEVM(p) as a levels-VAR(p+1) and then using the VAR's Matrix-Parameters as measures … so I could just normalize the cointegration vectors to length=1. …

process is convert it to a VAR model $$ y_{t} = \left(I+\Pi+\Gamma_{1}\right)y_{t-1}+\cdot\cdot\cdot+\left(\Gamma_{p-1}-\Gamma_{p-2}\right)y_{t-p+1}-\Gamma_{p-1}y_{t-p}+u_{t} $$ find the eigenvalues of … the companion matrix $$ \left[\begin{array}{ccccc} I+\Pi+\Gamma_{1} & \Gamma_{2}-\Gamma_{1} & \cdot\cdot\cdot & \Gamma_{p-1}-\Gamma_{p-2} & -\Gamma_{p-1}\\ I & 0 & 0 & 0 & 0\\ 0 & I & 0 & 0 & 0\\ 0 & …