Wald test eviews series#
Two things to note: (1) This will still be OK even if the data are stationary, so you can use the T-Y approach as an insurance policy, if you even "suspect" that one or more of the series may by I(1) or I(2) (2) This model in the levels, with the extra lags, is ONLY for causality testing. It's the ADDITION of the extra lags (that are NOT included in the formlulation of the test) that gets you the result you want. You fit the model in the levels (counter-intuitive, I know, if the data are non-stationary). That's all it is - a trick to "fix up" the distribution of the Wald test statistic so it is asymptotically chi square.
![wald test eviews wald test eviews](https://ritme.com/wp-content/uploads/2021/03/EViews12-Whats-New-FINAL-2020-2.jpeg)
Now, there are basically 2 equivalent ways to deal with this, the simpler of which to apply is the Toda-Yamamoto "trick". The distribution is non-standard and involves unknown "nuisance" parameters, so it can't be tabulated, and you don't have proper critical values to use - even with an infinite amount of data.
![wald test eviews wald test eviews](https://i.ytimg.com/vi/bByayyohNv4/maxresdefault.jpg)
If the data are non-stationary then the usual Wald test (or the LRT for that matter) for testing the restrictions involved in causality doesn't have its usual asymptotic (chi square) distribution. This is a great example of the books lagging behind the theory (and practice, actually). econometric texts don't cover it - you'd need to look at something like Helmut Lutkepohls' "Multiple Time Series" text. You won't find anything about this in any of the texts written prior to 1994. There's a bit more information in my April post (linked in the post above). (This last piece of information may provide a cross-check on your overall conclusions.)Īnaonymous: Thanks for the comment.
![wald test eviews wald test eviews](https://www.leblogdutesteur.fr/images/test-de-wald_9.jpg)
"If two or more time-series are cointegrated, then there must be Granger causality between them - either one-way or in both directions.
![wald test eviews wald test eviews](https://s3.studylib.net/store/data/005836947_1-19fab3966af86487e67f1d341e927d03.png)