EViews.com

Is a Wald test the only way to proceed here?

Are there any LR/LM tests that use other bits of the output? I do seem to be able to estimate the restricted specification even though I can't test for it outright using Wald.

I have something like: System: LEVPIIGS Estimation Method: Generalized Method of Moments Date: 03/18/10 Time: 20:34 Sample: 2001M09 2009M03 Included observations: 93 Total system (balanced) observations 455 Identity matrix estimation weights - 2SLS coefs with GMM standard errors Kernel: Bartlett, Ba...

The other diagnostic I get is:

"Positive or non-negative argument to function expected"

Sorry, very lazy of me Perhaps a more reasonable question is: When I get: "Restriction variance cannot be computed. Restrictions may not be unique." I'm not entirely sure what this means. My best guess is that I don't have enough degrees of freedom to test all the restrictions at the same ...

So I can just leave a space between each restriction then...I guess what I was doing wrong was hitting the enter key to separate out restrictions so I thought you couldn't enter more than one at a time. By the way...can I enter the coefficient restrictions into word and then paste them into the Wald...

I would like to test the hypothesis that all coefficients are the same across equations in a system e.g. if I have: y1=C(1) + C(2)x1 + C(3)z1 y2=C(4) + C(5)x2 + C(6)z2 I want to test C(1) = C(4), C(2) = C(5), C(3)=C(6) i.e. for panel structure (which has efficiency gains if the null is true I believ...

If my dependent variable is naturally I(0) but some of my independent variables are I(1), does this mean I cannot use this method then?

Does this procedure require all the variables i.e. both explanatory and dependent variables to be I(1)? I have a dependent variable which is I(0) and most of the explanatory variables are I(1).

From which version does Eviews begin to support this estimation method?

In a system, should be adding the AR(1) term individually for each equation then?

Thanks for all your help btw, you've been very quick with your replies

Both EViews method and iterated Cochrane-Orcutt work with a lagged dependent variable because both are nonlinear algorithms to minimize the sum of squared residuals. If the errors are normal, this is maximum-likelihood. Does the fact that I'm using the White robust covariance matrix make any differ...

Would it be possible for someone to explain very briefly why Eviews' method of estimation avoids the problems with lagged dependent variables that would normally afflict things like Cochrane-Orcutt. Or could you direct me to a paper or textbook?

I believe EViews algorithm is largely equivalent to iterative Cochrane-Orcutt. Both attempt to find local minima of the sum squared residuals. This will be very sensitive to the initial conditions right? But I wouldn't have any control over that anyway just by putting AR(1) as an explanatory variab...

Is this what is referred to in the manual as the "Marquadt Non-linear Least Squares Algorithm"?

I'm afraid I'm not familiar with the theory behind this thing, or why it should indeed be compatible with lagged dependent variables. What the best source of information?

Quite aside from the multiple solutions problem, am I correct in thinking that the estimates of the rho term in the presence of a lagged dependent variable would be inconsistent anyway under Cochrane-Orcutt since the first step OLS would be inconsistent? In which case I shouldn't be running a regres...