Hi there!
I found that sometimes different coefficient estimates are obtained when using cvmodel instruction to estimate the non-recursive SVAR. The situation is most troublesome particularly when I want to obtain the standard errors for those coefficient estimates which were obtained early by doing more piters in the preliminary estimation.
For example, in the output file attached, I estimate the model first by "cvmodel(parmset=kimroubi,pmethod=genetic,piters=1000,iters=2000,factor=afactor) vmat ab" and obtain the coefficient estimates which are closest to reasonableness. But when I reduce the number of piters to 100 in order to obtain the standard errors for those coefficent estimates, I found it's very hard to obtain the same coefficient estimates.
Could you please help me to solve this problem?
Code, data and output files are attached
Best Regards
The estimation of SVAR
The estimation of SVAR
- Attachments
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- coefficient estimates.txt
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- Input_w_m1_crncy_95m7_09m9.xls
- (91.5 KiB) Downloaded 650 times
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- code.txt
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Re: The estimation of SVAR
Your model isn't identified. How is it that you plan to distinguish the first two equations in the SVAR?
Re: The estimation of SVAR
Hi Tom!
Yes, I know the model hasn't been properly identified. I'm still in the process of experimenting some variation in the non-recursive identification restrictions to see if ms shock, md shock, and e/r shock can be identified. At this stage, I need to obtain correct coefficient estimates (with expected sign and reasonable magnitudes) and their standard errors for each trial.
Yet I found it sometimes is impossible to obtain the standard errors for those coefficient estimates which were found most reasonable by doing more piters in the earlier estimation. When I switch to use the instruction "cvmodel(parmset=kimroubi,pmethod=genetic,piters=100,iters=2000,factor=afactor) vmat ab", RATs gave different coefficient estimates (although the signs remain unchanged, the magnitudes are different) from that using cvmodel(parmset=kimroubi,pmethod=genetic,piters=1000,iters=2000,factor=afactor) vmat ab". But I still want RATs to deliver the same coefficient estimates which I obtained with the latter instruction together with the associated standard errors. This is the problem I met. Could you please give some advice to solve this problem?
Best Regards
Yes, I know the model hasn't been properly identified. I'm still in the process of experimenting some variation in the non-recursive identification restrictions to see if ms shock, md shock, and e/r shock can be identified. At this stage, I need to obtain correct coefficient estimates (with expected sign and reasonable magnitudes) and their standard errors for each trial.
Yet I found it sometimes is impossible to obtain the standard errors for those coefficient estimates which were found most reasonable by doing more piters in the earlier estimation. When I switch to use the instruction "cvmodel(parmset=kimroubi,pmethod=genetic,piters=100,iters=2000,factor=afactor) vmat ab", RATs gave different coefficient estimates (although the signs remain unchanged, the magnitudes are different) from that using cvmodel(parmset=kimroubi,pmethod=genetic,piters=1000,iters=2000,factor=afactor) vmat ab". But I still want RATs to deliver the same coefficient estimates which I obtained with the latter instruction together with the associated standard errors. This is the problem I met. Could you please give some advice to solve this problem?
Best Regards
Re: The estimation of SVAR
You do realize that not identified means that there are at least two modes with identical log likelihoods, don't you? It's quite possible that one of the two is on a very small "hill" and the other is on a bigger one, so almost any initial conditions will end up at the latter rather than the former.