   VAR and VECM model

1. Variables should be stationary in VAR model
Professor Rapelanoro Nady commented as such > You should use stationary VAR, your
variables should Be stationary.

2. Error correction term should not exceed -1.
Professor Olasehinde Timilehin commented as such > The moment the error term exceeds -1 it
out of the game... It will explode and this has no economic significance...please Hamilton text
book on time series using.

3. VECM (restricted VAR) will automatically convert your variables into first difference
Professor Sayed Hossain commented as such> Suppose your variables are I(1). While running
VECM, use non-stationary level data as VECM will automatically convert all variables into first
difference.

4. VAR (unrestricted VAR) will not automatically convert your variables into first
difference.
Professor Sayed Hossain commmented as such> Normally we use stationary data in VAR model.
Suppose your variables are I(1). While running VAR model, use stationary data as VAR will not
automatically convert all variables into first difference.

5.Which model to be used?
Professor Muili Adebayo Hamid commented as such> According to lutkepohl VAR IS THE LEVEL
version of VECM. to use var series need to be stationary. however, if the series is stationary after
differencing you use VECM. if you variable Is stationary you can use var Granger causality but if
it's not stationary you can use toda yamamoto causality.

6. Microfit 5 is suitable for short run and long run model
Professor Abubakar Kumo commented as such >> To make it easier for you, look for Microfit 5.
There, the long run and short run are separated for you by the software.

7.What should I do next?
Imrn Rjn posted following VAR figure and asking for interpretation.

Sayed Hossain commented> It is a unrestricted VAR (not VECM). Now go for diagnostic checking
of this model.

Imran Rjn commented>  If i am not wrong then you mean to say, residual test which include
normality, autocorrelation LM Test, Portmanteau Test and Heteroskedasticity test...

Sayed Hossain Yes

Waseem Khan commented> In above figure two equation are given in one equation D(SP) is
dependent in second D(IR) is dependent.... These are short run or long run equations????

Sayed Hossain commented> short run as it is unrestricted VAR model.

8. Error correction term
Chetan Gk‎ posted the ECM figure below.

Sayed Hossain commented> Here C(1) is error correction term. As the C(1) is negative but not
significant, it means that there is no long run causality running from independent variables to
dependent variable. It also fails to suggest the validity of long run relationship between variables,
LS and LF. And here C(2) until C(5) coefficients are all short run coefficients. Here none of the
coeffients are signoficant.    POPOLAR BLOGS
Dave
Meo School of Research
Shishir Shakya
Noman Arshed
VAR and VECM model
 Univariate Models     Multivariate Models
 Panel Data Model 9. Interpret the results taken from VECM system equation?
Professor Kanika Chawla Anand posted it.

D(EXPO) = C(1)*( EXPO(-1) - 30.9360239443*OFDI(-1) + 698.667289493 ) + C(2)*D(EXPO(-1)) +
C(3)*D(EXPO(-2)) + C(4)*D(OFDI(-1)) + C(5)*D(OFDI(-2)) + C(6)

D(OFDI) = C(7)*( EXPO(-1) - 30.9360239443*OFDI(-1) + 698.667289493 ) + C(8)*D(EXPO(-1)) +
C(9)*D(EXPO(-2)) + C(10)*D(OFDI(-1)) + C(11)*D(OFDI(-2)) + C(12)

Moulana Naykrasyvishyy Cholovik commented>  if C(1 ) is negative and significant, then there is a
long-run causality running from OFDI to EXPO; and if C(7) negative and significant a long-run
causality running from EXPO to OFDI. if both are negative and significan then there is bi-directional
causality.

10. What are the differences between VAR (unrestricted VAR) and VECM (restricted VAR)?

Sayed Hossain commented> VECM has an error correction term while VAR does not have.
Otherwise, they are same. Suppose you have three variables, Y, X1 and X2. Whatever the order
you set to estimate VAR model in EVIEWS or STATA, results will be always same. But in case of
VECM, if you change the order of variables in EVIEWS or STATA, results will change substantially.