Dear all,
Granger causality test is applied on non stationary data or stationary data?
VAR is applied on stationary or non stationary data?
Like wise VECM is applied on stationary and non stationary data?
in eviews when
we run VAR there is tab option (lags intervals for endogenous)
we run VECM there is tab option(lags intervals for D(endogenous))
What do they mean? if we choose 1-1 or 0-0
Thanks and regards..
Ahmad is correct but I think a mixture of I(1) and I(0) makes more sense
It can be implemented in both stationary and non stationary data
It can be applied to both but the varaiables have to be stationary at same level pls they have to be cointegrated. You can see my publication "An Empirical Investigation of Foreign Aid and Dutch Disease: Evidence from Bangladesh."
1. Granger Causality tests try to determine if one variable(x1) can be used as a predictor of another variable(x2) where the past values of that another variable may or may not help. This means that x1 explains beyond the past values of x2. Two important assumptions here are both x1 and x2 are STATIONARY and there exists a linear relation between their current and past values. This means that if x1 and x2 are non-stationary, you have to make them stationary before testing for Granger Causality. So, it can be applied in both stationary and non-stationary cases, but you have to make the data stationary before using it.
2&3. VAR or VECM - Cointegration is a phenomenon where a linear combination of two or more non-stationary series becomes a stationary series (or non-stationarity of higher order reduces to a lower order of non-stationarity). When you are unsure of true relationship between two variables about which is causing which one, you can try to test(Johansen test) for Cointegration and if the cointegration exists, then you may try and fit VECM otherwise you need to fit VAR.
4. For this question, I suggest you better try to refer Eviews guide and VAR/VECM equations you want to fit.
(needless to say that I disagree with the suggestion that Granger Causality can be used in both stationary and non-stationary cases, the data should be stationary, if not, you have to make it stationary before using Granger Causality)
For All There is somewhat controversial responses to the question. So, I am requesting to all experts to provide their responses by citing the respective articles or books. Thank you!
Granger causality and VAR models can be applied on both stationary and non stationary data, but the variables must be cointegrated otherwise the causality or the model could contain spurious relations between variables. Nonetheless as econometry is not so rigid as some think, there are lots of literature that show the cointegration as a theretic construct and a risk to drop significant variables that would complement the studies, for more of this i would recommend this book
https://www.bde.es/f/webbde/SES/Secciones/Publicaciones/PublicacionesSeriadas/DocumentosTrabajo/93/Fich/dt9314e.pdf
Teena ... for 1 & 2, you can use both I(0) and I(1) variables. If you have mixture both variables, i suggest to go for Toda-Yamamoto (1995) augmented Granger causality test which is independent of order of integration and possible co-integration.. 1-1 and 0-0 simply means that, the lag length.
VAR is run on level variables, they can be stationary or non-stationary
Granger-causality testing in a VECM (that has short-run and long-run components) assumes all the data are stationary by either cointegration or differencing transformations. If there is no cointegration the error-correction term can be excluded and testing is conducted by a Wald test in a stationary VAR. Alternatively, you can use the Toda and Yamamoto (1995) surplus lag Granger-causality test that uses nonstationary data. See Dave Giles blog for a discussion of this - the link is given below. https://davegiles.blogspot.com/2011/04/testing-for-granger-causality.html?showComment=1320248137553#c4210543007105661619 This method means you do not need to consider the notions of long-run and short-run Granger-causality. This is useful if you simply wish to know whether Y Granger-causes Y for example. In a VECM long-run Granger-causality is tested using the t-ratio on the error-correction term in each equation. Short-run Granger-causality uses Wald tests to set the coefficients on all lagged differences of on variable to zero in an equation. In EViews I think this needs to be implemented using the make model/system proc.
Granger causality (1969) requires both series to be stationary. Toda-Yamamoto causality requies no such criteria, the test can be applied to both stationary and non stationary data.
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