Toda and Yamamoto causality test

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startz
Non-normality and collinearity are NOT problems!
Posts: 3376
Joined: Wed Sep 17, 2008 2:25 pm

Re: Toda and Yamamoto causality test

Postby startz » Sun May 06, 2018 11:50 am

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elmst616
Posts: 32
Joined: Fri Apr 29, 2016 2:36 pm

Re: Toda and Yamamoto causality test

Postby elmst616 » Sun May 06, 2018 12:46 pm

sam SAM wrote:Hello thank you very much

I feel that the site is not updated?

Cordially


I don't understand why you would need the blog post to be updated. The method has not changed since year 1995. The blog lays out very clear instructions on how to step by step implement the T-Y method. You don't even need to program. Just point and click.

elmst616
Posts: 32
Joined: Fri Apr 29, 2016 2:36 pm

Re: Toda and Yamamoto causality test

Postby elmst616 » Sun May 06, 2018 12:48 pm

sam SAM wrote:I do not know where the problem is (at home)!!!


Looking to help out as I know how frustrating it can be. But I don't understand your comment. You mean you can't find the instructions?

terrya
Posts: 107
Joined: Wed Aug 26, 2009 2:37 pm

Re: Toda and Yamamoto causality test

Postby terrya » Sun May 06, 2018 6:38 pm

Look at Dave Giles post for Friday, April 29, 2011

terrya
Posts: 107
Joined: Wed Aug 26, 2009 2:37 pm

Re: Toda and Yamamoto causality test

Postby terrya » Sun May 06, 2018 6:45 pm

Further point:

This is what Tom Doan says about the TY approach:
"We've had several recent questions about the use of the Toda-Yamamoto variant on causality tests (including one user who had a referee insist that he use it). The reference on this is

Toda, H.Y., Yamamoto, T., 1995. Statistical inference in vector autoregression with possibly integrated processes. Journal of Econometrics 66, 225-250.

and, in short, it tests for causality by adding lags to a VAR (to allow for possible unit roots/cointegration) and then tests zero restrictions which don't include those added lags.

Upon taking a careful look at the paper, my reaction was that the result seemed "fishy." I discussed this with a well-known econometrician whose reaction was a bit stronger than that. (I think it's safe to say that he was surprised it ever got published). Despite that fact that there are countless examples of its use in the literature, it is *not* a way around the problem of a non-standard distribution for the causality tests. Rather than fixing the Sims-Stock-Watson problem, it actually confirms their results that most coefficients and linear combinations thereof are asymptotically normal, but that certain restrictions which eliminate channels of influence for unit roots aren't.

By adding extra lags and then not testing them, the bad behavior is shifted onto the untested lags. In effect, you're no longer testing lack of causality, since that requires testing all the lags. While the test statistic has the correct distribution under the null (which the SSW results would predict), it will suffer badly from lack of power since, if the coefficients aren't, in fact, zero, the "causality" will get shifted fairly easily to the untested lag(s) since an integrated process is so highly autocorrelated."

And further: "The "point" of the TY test is that it has standard asymptotics even if the series have unit roots, while the standard Granger test doesn't. If you're bootstrapping anyway, you don't care about standard asymptotics, so there's no reason not to use the standard exclusion test."


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