Identification of cointegration vectors
Posted: Fri Aug 05, 2016 3:05 pm
I am empirically testing the Monetary Approach to Exchange Rate Determination model w.r.t UK's nominal exchange rate, and using US as the foreign country. In accordance with Johansen's cointegration rank test, 2 possible cointegrating vectors were discovered. I attempted to identify the two equations based on economic theory. The first Cointegration Vector (CV) is of course normalised w.r.t exchange rate, and the second CV (which I am not interested in but I am aware it still has to make economic sense since it impacts on the first CV's coefficients) is normalised w.r.t to money demand (while restricting exchange rate to equal zero). Now, with the identification, literature suggests imposing restrictions on the proportionality between relative monies and exchange rate, as well as the symmetry (equal and opposite signs) of the domestic and foreign outputs and interest rate. This is where I am having problems with. I do not wish to impose these restrictions since they will force the coefficients to be the same for the domestic and foreign variables. However, I want to have unique coefficients for the individual variables.
So my question is, what other restrictions can I make in order to satisfy both the order and rank conditions for identification? I have tried to impose weak exogeneity on both CVs but Eviews will not give me t-statistics, reporting that one of the CV is not properly identified. It could be that my numerical restrictions are wrong (see below), I don’t know. Interestingly however, Eviews produces t-statistics (meaning it believes the CVs are identified) when weak exogeneity is imposed on the second CV only. Below is my estimating equation and the restrictions I imposed.
My model is as follows:
Z=(e,m,m*,y,y*,i,i*) denoting nominal exchange rate, UK (domesic) money supply, US (foreign) money supply, UK output, US output, UK interest rate, US interest rate – respectively.
Normalisation and Restrictions (weak exogeneity) used:
B(1,1)=1
B(2,1)=0
B(2,2)=1
A(3,1)=0,A(5,1)=0,A(7,1)=0,A(3,2)=0,A(5,2)=0,A(7,2)=0
Imposing the above restrictions does not produce t-statistics. However, the following does, but I do not understand the intuition, since there are no restrictions on CV 1.
B(1,1)=1
B(2,1)=0
B(2,2)=1
A(3,2)=0,A(5,2)=0,A(7,2)=0
Thanks for your assistance.
So my question is, what other restrictions can I make in order to satisfy both the order and rank conditions for identification? I have tried to impose weak exogeneity on both CVs but Eviews will not give me t-statistics, reporting that one of the CV is not properly identified. It could be that my numerical restrictions are wrong (see below), I don’t know. Interestingly however, Eviews produces t-statistics (meaning it believes the CVs are identified) when weak exogeneity is imposed on the second CV only. Below is my estimating equation and the restrictions I imposed.
My model is as follows:
Z=(e,m,m*,y,y*,i,i*) denoting nominal exchange rate, UK (domesic) money supply, US (foreign) money supply, UK output, US output, UK interest rate, US interest rate – respectively.
Normalisation and Restrictions (weak exogeneity) used:
B(1,1)=1
B(2,1)=0
B(2,2)=1
A(3,1)=0,A(5,1)=0,A(7,1)=0,A(3,2)=0,A(5,2)=0,A(7,2)=0
Imposing the above restrictions does not produce t-statistics. However, the following does, but I do not understand the intuition, since there are no restrictions on CV 1.
B(1,1)=1
B(2,1)=0
B(2,2)=1
A(3,2)=0,A(5,2)=0,A(7,2)=0
Thanks for your assistance.