Determining cointegrating vectors with Johansen
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Determining cointegrating vectors with Johansen
After running a cointegration test I get the results of the trace and eigen test. I see there are two cointegrating vectors, but how can one tell which are these two vectors just by looking at the output table? The help guide in Eviews doesn't seem to have any section on how to interpret results.
Re: Determining cointegrating vectors with Johansen
same question
Re: Determining cointegrating vectors with Johansen
If you scroll down the output of Johansen Cointegration Test, you'll see the cointegration equations at the bottom. Another way is to estimate the VEC and change the number of cointegrating vectors (i.e. rank) from within the VAR Specification window under the Cointegration tab. Estimation output will also display the cointegration equations.
Re: Determining cointegrating vectors with Johansen
I estimated the VEC and got one CE. In the eviews help guide it says that when estimating VEC, the number of CEs should be one lower than the number of explanatory variables? Why is this requirement so?If you scroll down the output of Johansen Cointegration Test, you'll see the cointegration equations at the bottom. Another way is to estimate the VEC and change the number of cointegrating vectors (i.e. rank) from within the VAR Specification window under the Cointegration tab. Estimation output will also display the cointegration equations.
And in the case where one gets 2 CEs from Johansen, but applies VEC to only 1 CE (given the eviews guide rule about one less CE than total explanatory variables) , how does Eviews decide to which CE to apply the VEC estimation?
Re: Determining cointegrating vectors with Johansen
Because the EC matrix is not of reduced rank and thus a levels VAR is appropriate. The following is the usual rule of thumb:In the eviews help guide it says that when estimating VEC, the number of CEs should be one lower than the number of explanatory variables? Why is this requirement so?
rank(pi)=rank(X) <-- level VAR
0<rank(pi)<rank(X) <-- CVAR
rank(pi)=0 <-- VAR in first differences
More here: http://www.econ.ku.dk/okokj/papers/kjdhengii.pdf
HTH
Alan
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