Dear all,
here I am again for your help.
I have estimated a VAR, actually a Structural VAR but I don't think this makes a difference in relation to my question.
I want to compute Cumulative Orthogonal Impulse Response Functions based on Parametric-Bootrastrap. This is done with this line in STATA:
irf create sVAR_US_A6, step(12) replace est(sVAR_US_A6) bsp bsaving( bsp_sVAR_US_A6, replace)
Then I generate the IRF table with this command:
irf table coirf, irf(sVAR_US_A6) impulse(mmXRq) response(D.Lgdp_nc3) std
the output is:
_
Results from sVAR_US_A6
step coirf Lower Upper S.E.
0 0 0 0 0
1 0.000053 -0.00067 0.000775 0.000369
2 -0.000018 -0.001206 0.00117 0.000606
3 -0.000132 -0.001727 0.001464 0.000814
...
step | coirf | Lower | Upper | S.E.
12 | -0.001106 | -0.004275 | 0.002064 | 0.001617
_
200 replications for the bootstrap. The SE is calculated as the standard deviation of all the bootstrap IRFs by Step
I do not get how the Confidence Intervals are generated given the IRF value and its SE. I have tried the cii command:
cii 200 -0.001106 0.001617
but this is what I get:
Variable | Obs Mean Std. Err. [95% Conf. Interval]
-------------+---------------------------------------------------------------
| 200 -.001106 .0001143 -.0013315 -.0008805
Does anyone can help?
Does it matter they are Cumulative IRFs?
Thanks a lot.
Dear Gianluca,
As far I know, SVAR models have different framework than the standard VAR models. However, before the interpretation of the model, you need to determine to lag length and the restrictions on the short run or long run recursive matrix. And you do not need to follow cumulative impulse response matrix due to the SVAR models are based on structural factorization. I recommend to use Eviews, if possible, for SVAR models due to it has a user- friendly framework. I do not know your model, my recommendations are limited. Maybe this link will help you
https://www.google.com.cy/url?sa=t&rct=j&q=&esrc=s&source=web&cd=9&cad=rja&uact=8&ved=0ahUKEwjGksergLPTAhXGuBoKHV2zBmgQFghhMAg&url=http%3A%2F%2Fwww.hkimr.org%2Fuploads%2Fconference_detail%2F849%2Fn_paper_0_105_14may-ccbs_docs_9918_1-doc.doc&usg=AFQjCNEb1dharc3r7eJWwITcyjgZr6wGDg
Have you considered using Loal Projections method by O. Jordà (2005) to obtain the IRFs
I found the answer myself a couple of weeks ago. I report it here for the sake of completeness, just in case someone'll have the same problem:
1. Calculate the Standard Deviation (SD) of the Bootstrap Distribution. That approximates the Standard Error of the IRF. In this case, for the 12th step, it is 0.001617
2. The lower bound CI is: EST - (1.96*SD) =>
-0.001106 - (1.96*0.001617)=-0.004275
3. The upper bound CI is: EST + (1.96*SD) =>
-0.001106 + (1.96*0.001617)=0.002064
-/+1.96 is the 2.5/97.5 quantile of the Normal Distribution.
Hence, these are 95% Normal-Approximation Bootsrap-based Confidence Intervals.
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