Hi,
As is known in the literature, the cointegration is investigated for a set of integrated series (at least one unit root) which are in comovement (Granger). However, if all of series are stationary (they have no trend and therefore type I (0)) the cointegration does not exist by definition in this case. The estimation of an VECM for example is reduced to the estimation of a VAR so in my opinion we don't have to go for the cointegration analysis but the causalité analysis can be conducted in the two cases.
Best regards.
No You can do a simple regression analysis , or Vector Auto Regression .
Hi,
As is known in the literature, the cointegration is investigated for a set of integrated series (at least one unit root) which are in comovement (Granger). However, if all of series are stationary (they have no trend and therefore type I (0)) the cointegration does not exist by definition in this case. The estimation of an VECM for example is reduced to the estimation of a VAR so in my opinion we don't have to go for the cointegration analysis but the causalité analysis can be conducted in the two cases.
Best regards.
No. If all the variables are stationary, you just go ahead with the normal simple regression techniques
A VAR will not identify simultaneous contemporaneous effects. You should examine the model for seemingly unrelated time series effects as well. You need to examine it for structural breaks and seasonality before you are finished with your modeling.
Run simple regression analysis , and then the Durbin - Watson auto correlation test is not applicable and no need to worry about auto correlation in regression results , as the variables are stationary.. If you want to test causality, use VAR at level form and apply Granger causality test . Here Vector Error Correction Model for Granger causality is not done as cointegration can not be done .
No,my previous researchers are very much. correct. Moreover for this question, one should more practice with econometric software. Learning by doing.
If all variables are stationary at level, this means there's no long run relationship, a short run relationship may exist and no need for cointegration estimation. But as said above you may need to investigate for causality between them.
Thank you all for giving me your valuable suggestions. It is also helping me to find appropriate text
You cannot go for cointegration. But you check ARDL Bound test for cointegration i guess if ypur data are stationary at level
you mean actual or transformation data, if actual data you can not go to cointegration
if all variables are stationary at level, then that means no co-integration between these times series. you could do further analyses using ARIMA and VAR models.
if all the variables are stationary at level n u want to run cointegration then you cal use ARDL
If the variables are stationary at levels, in this situation regression results are valid in this situation. When variables are unit root at level in this situation regression results are spurious. When the results are valid then no need for co-integration. The idea of co-integration is for integrated series.
Yes, Dr. Irfan Malik is right to my mind The idea of co-integration is for integrated series at first difference level.
No, atleast one of the series should contain unit root. If all series are stationary, then no-conintegration, you simply apply VAR
What happens if 3 variables are not stationary at level and one of the independent variable is stationary, can I still do the VECM
When you have a mixture of I(1) and I(0) variables in your VAR, it's advisable to use the bounds testing procedure of Perasan, Shin & Smith (PSS) (2001).
An Autoregressive Distribution Lag model is the best for this analysis..
Yes it is the right answer that if variables are all stationary, either OLS , or VAR can be directly applied .Granger causality test can be directly done at level form.data with out any transformation. Cointegration and Error Correction models have not to be applied and not relevant here with Stationary level form data .
I found this useful: Article Statistical Inference in Vector Auto Regressions with Possib...
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