You can use ARDL in time series data if u use by eviews 9. Eview 8 can't run ARDL.
Besides, ARDL can use I (o) also and I(1) and panel data and time series also. However before running ur time series, you should run and check ur individual data at original data by uni root test.
Yes dear Victor. You can use ARDL in case of all I(1) variables.
You can actually use ARDL on 1(0) and 1(1), or 1(0) alone, 1(1) alone. But ARDL breaks down when you have 1(2) variables. E-view 9 remains the your best option.
Yes you! ARDL assumes that variables are either I(0) or I(1) or a mixture of both. I recommend eviews 10 for a better work.
ARDL model can use time series data and panel data also, but be4 we are running, we need to check at unit root test with I(0) or I(1) or I(2). That meaning ur data is stationary which level data I(0) or 1st different data I(1) or 2nd different I(2)? Then u identified already, u can use it ARDL. The lag also can use. If u use a lag model, pls read it back the theory of the distributed lag model. Tq.
Aye Aye Khin Thanks for your response, it was invaluable. I was just recently introduced to ARDL, getting your feedback was so helpful. Thanks.
ARDL can of course be use if all your variables are I(1). If all your variables are I(1) ARDL is only valid if there is at most 1 cointegrating relationship between the I(1) variables, If there are more than one the method is not valid. In addition all the I(1) variables except one obey are at least weakly exogenous. To verify this you must use a multivariate methodology such as Johansen and only if the weak exogeneity conditions are satisfied return to single equation estimation
ARDL is applicable for modeling all I(1) variable and a combination of I(1) and I(0).
In situations where you are modeling all I(1) variable and a combination of I(1) and I(0), ARDL very much applicable
There is a misconception about ARDL that I see very common. although, ARDL is desigend to be used in a case of mixture of I(1) and I(0) variables, the test itself is not a theory by its self but an another test with a different distribution of the a long run relationshep between the variables of interest. Therefore, if all the variables included are knowen to be I(0), then their linear combination is I(0) by difinition. Hence, no cointegration can exists beteween them.
Yes, ARDL can be applied on series that are all stationary at I(0), I(1) and mixture of the two. However, ARDL bound test is appropriate when series are of mixed order of integration (I(0) and I(1)), but Johansen cointegration is appropriate for series that are all stationary at first difference I(1).
Yes, provided that no series is integrated of order 2. However, ARDL bound test is appropriate when series are of mixed order of integration (I(0) and I(1)), but Johansen cointegration is appropriate for series that became stationary at first difference I(1).
Hamed Alhoshan is correct in his reference to the ARDL bounds test. It is a test of cointegration and an estimator of the cointegrating relationship. It can be used when variables are I(1) or I(0), the explanatory variables are weakly exogenous and there is only one cointegrating relationship. The ARDL bounds test is available in several econometric packages. It is very easy to implement but far too little attention is paid to the necessary underlying conditions. In particular, many applications have obvious theoretical feedback from the dependent variable to the explanatory variables.
ARDL (or ADL) stands for autoregressive Distributed lag. It is a standard model for estimating dynamic economic relationships (Models that include lagged dependent and lagged dependent variables and the variables are stationary). the current edition of Stock & Watson's introductory econometrics text contains a good account of ARDL models.
If you look at the underlying equations in the ARDL bounds test the variables are first differences of I(1) variables (stationary) and a possible cointegrating relationship which is stationary. The ARDL bounds test is just an application (interpretation) of the ARDL model. An understanding of ARDL models will enhance your understanding of the ARDL bounds test
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