VAR and VECM model

variables should Be stationary.

out of the game... It will explode and this has no economic significance...please Hamilton text

book on time series using.

VECM, use non-stationary level data as VECM will automatically convert all variables into first

difference.

difference.

Suppose your variables are I(1). While running VAR model, use stationary data as VAR will not

automatically convert all variables into first difference.

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.

There, the long run and short run are separated for you by the software.

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.

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.

Ade Kutu

Afolabi Luqman

Abdullah Sonnet

Asad Zaman

Atiq Rehman

Burcu Özcan

Ghumro Niaz Hussain

Muhammad Anees

Mohammad Zhafran

Muzammil Bhatti

Monis Syed

Mine PD

Moulana N. Cholovik

Muili Adebayo Hamid

Nicat Gasim

Najid Iqbal

Nasiru Inuwa

Noman Arshed

Rapelanoro Nady

Seye Olasehinde-Williams

Suborno Aditya

Sayed Hossain

Shishir Sakya

Sheikh Muzammil Naseer

Tella Oluwatoba Ibrahim

Younes Azzouz

Meo School of Research

Shishir Shakya

Noman Arshed

VAR and VECM model

Hossain Academy Note

Univariate Models |

Multivariate Models |

Panel Data Model |

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.

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.