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Long run restrictions for a structural VAR

Posted: Fri Mar 30, 2018 7:38 am
by Zankawah
Dear Colleagues,

I have estimated a structural VAR model and I am imposing short run and long restrctions on the model using eviews. However, I am having difficulties with estimating the long run restrictions because eviews seems to impose a lower triangular matrix on the long run restriction. Because my long run restricted model allows some coefficients to be estimated in the upper triangular matrix, I am getting an error message saying "the structural VAR objective function cannot be evaluated at the initial parameter values". I want to ask if anyone have an idea as to how I can overcome this problem?

Thank you

Re: Long run restrictions for a structural VAR

Posted: Mon Apr 02, 2018 8:54 am
by EViews Matt
Hello,

There's no implicit restriction form on the long-run matrix, you can restrict any element(s) you wish as long as you completely identify the system. That error can occur when, for example, the choice of initial values for the estimation happens to produce a singular matrix or other numerically troublesome result. Have you tried starting the optimization with a different/random set of initial values?

Re: Long run restrictions for a structural VAR

Posted: Wed Apr 04, 2018 5:33 am
by Zankawah
Thank you for the reply. You asked if I tried starting the optimization using different initial values. The issue is that I am not sure what the initial values are, and this may be the problem. So I want ask what exactly are the initial values? You also mentioned that there are no implicit restrictions in the long run matrix as long as the system is completely identified. Are you referring to the identification of the short run matrix or the long run matrix? The identification scheme used to identify both matrices is based on theory and previous literature, and the results show that the model is just identified. Am not sure if there is something else that I am not doing right.

Re: Long run restrictions for a structural VAR

Posted: Wed Apr 04, 2018 9:08 am
by EViews Matt
Lets start with the initial value issue, since that's easy to fix (if it's the cause of your difficulty). When estimating an SVAR, EViews uses an initial value of .1 for all estimates by default. If you open your VAR, go to Proc -> Estimate Structural Factorization, and switch to the Optimization Control tab, I assume the Starting Values option is set to Fixed: 0.1. Try reestimating your SVAR with one of the Drawn from... options instead. Those options assign random values to the initial estimates, which may avoid numerical special cases triggered by all .1's. If you're running your SVAR estimate from the command window or a program instead of the GUI, you can add the f0=u or f0=n option to the svar proc.

Re: Long run restrictions for a structural VAR

Posted: Thu Apr 05, 2018 3:29 pm
by Zankawah
Thank you once again for the guidance and suggestions. I have tried the options you suggested from the Drawn from... and when I estimate the long run F matrix with some coefficients in the upper diagonal, I still get an error message saying "Optimization may be unreliable (first or second order conditions not met)". What I noticed however, is that the long run impulse response matrix F has a bracket where it states 'triangular'. To be specific, the long run matrix is stated as "Recursive long-run impulse response (F triangular)". The other matrices such as the A and S matrices have no any such restrictive definition and with those matrices, it is easy to estimate coefficients on either side of the diagonal. I am therefore, wondering whether the long run matrix is just programmed to be lower triangular such that all coefficients in the upper diagonal and cannot be estimated (I may be wrong, but this is just my little thought).

Re: Long run restrictions for a structural VAR

Posted: Thu Apr 05, 2018 3:59 pm
by EViews Matt
Those "restriction presets" are just simple, common sets of restrictions that can be customized further. You're not limited by those presets, e.g., the F matrix doesn't need to be triangular. The new error message you're receiving could be caused by many things, such as bad initial values or poor identification of the SVAR model. Reestimating with different initial values may produce better results if the former is true, while changing the restrictions you're imposing can address the latter.

SVAR Matrices

Posted: Sat Apr 14, 2018 3:01 pm
by Gecon
Hello,

I'm working on SVAR to estimate my model, I found this eviews video (note below link) very helpful however I need to know how to determine MatrixA and MatrixB as mentioned in this video. I have 1 dependent variable and 6 independent variables.

https://www.youtube.com/watch?v=JFKZn665D8c&t=51s

Thank you very

Re: Long run restrictions for a structural VAR

Posted: Mon Apr 16, 2018 9:03 am
by EViews Matt
Hello,

Are you asking about how you should restrict those matrices? There is no one correct way to do so, the restrictions are part of the assumptions of your model. It's up to you to specify how you believe the variables in your model are related to one another.