### GMM issue when estimating LR coefficients directly (ECM)

Posted:

**Sat Nov 18, 2017 1:08 am**Hello,

I am using EViews 9.5 and trying to estimate multiple error correction models by GMM. The loop generates equations and then deletes all equations having at least on insignificant coefficient (excl. a constant). The equation is written in the following form allowing me to see long-run coefficients immediately (I need this because I will have to impose homogeneity on the two coefficients in long run):

dlog(x)=c(1)+c(2)*dlog(z1)+c(3)*dlog(z2)+c(4)*dlog(k1)+c(5)*dlog(k2)-c(11)*[log(x(-1))-c(12)*log(z(-1))-c(13)*log(k(-1))] (z=z1+z2 and k=k1+k2)

The problem with this approach is that in vast majority (sometimes all) equations p-values almost reach 1 and parameters assume values well above one. On the other hand, it estimates the equation well (no issues with p-values or the size of coefficients) if I simply estimate the following (thus, in order to get LR parameters I have to divide c(12) and c(13) by -c(11):

dlog(x)=c(1)+c(2)*dlog(z1)+c(3)*dlog(z2)+c(4)*dlog(k1)+c(5)*dlog(k2)+c(11)*log(x(-1))+c(12)*log(z(-1))+c(13)*log(k(-1))

Thus, in the first option the loop deletes good equations due to some estimation issue when imposing restrictions/non-linearity in coefficients. It sometimes also gives me "near singular matrix" error and after doing some reading on this forum, I tried to re-set parameters before estimation by "param" command. However, it did not help me.

I do not understand what is going, the models are fine per se and it is just when I specify explicitly LR coefficients in an equation. Could you please give me some advice what else I could do? It is important for me to keep the specification as in the first equation - I need to set c(13) as (1-c(12)) and it obviously brings about the same problem as the first equation above.

Thank you,

Z

I am using EViews 9.5 and trying to estimate multiple error correction models by GMM. The loop generates equations and then deletes all equations having at least on insignificant coefficient (excl. a constant). The equation is written in the following form allowing me to see long-run coefficients immediately (I need this because I will have to impose homogeneity on the two coefficients in long run):

dlog(x)=c(1)+c(2)*dlog(z1)+c(3)*dlog(z2)+c(4)*dlog(k1)+c(5)*dlog(k2)-c(11)*[log(x(-1))-c(12)*log(z(-1))-c(13)*log(k(-1))] (z=z1+z2 and k=k1+k2)

The problem with this approach is that in vast majority (sometimes all) equations p-values almost reach 1 and parameters assume values well above one. On the other hand, it estimates the equation well (no issues with p-values or the size of coefficients) if I simply estimate the following (thus, in order to get LR parameters I have to divide c(12) and c(13) by -c(11):

dlog(x)=c(1)+c(2)*dlog(z1)+c(3)*dlog(z2)+c(4)*dlog(k1)+c(5)*dlog(k2)+c(11)*log(x(-1))+c(12)*log(z(-1))+c(13)*log(k(-1))

Thus, in the first option the loop deletes good equations due to some estimation issue when imposing restrictions/non-linearity in coefficients. It sometimes also gives me "near singular matrix" error and after doing some reading on this forum, I tried to re-set parameters before estimation by "param" command. However, it did not help me.

I do not understand what is going, the models are fine per se and it is just when I specify explicitly LR coefficients in an equation. Could you please give me some advice what else I could do? It is important for me to keep the specification as in the first equation - I need to set c(13) as (1-c(12)) and it obviously brings about the same problem as the first equation above.

Thank you,

Z