FAVAR add-in

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Posts: 22
Joined: Sat May 04, 2019 11:23 am

Re: FAVAR add-in

Postby heer0 » Thu Aug 15, 2019 1:11 pm

daisysunf18 wrote:Hi,I am confuse the impulse variable (optional)what should I choose , I do not see it in favar.package. thanks in advance.

The impulse command is thought to depcify the impulse in case of multiple schock variables. If you only include one shock variable in your model, this command is redundant.

Hope it helps.

Posts: 3
Joined: Thu Aug 01, 2019 4:27 am

Re: FAVAR add-in

Postby daisysunf18 » Sun Aug 18, 2019 1:04 am

HI,I have another problem, I am doing an impulse response comparison chart. I use the same impulse variable ,same setting like number of lag、factor 、XDATA、XSLOW ,but different main variables(xir),At last,I have the same impluse response diagram,What is the cause of this? thanks in advance :D

Posts: 1
Joined: Tue Sep 17, 2019 3:20 am

Re: FAVAR add-in

Postby MG17 » Tue Oct 01, 2019 6:08 am

Hi everyone,

I recently started working with EViews and I really wanna express my gratitude to dakila for providing the FAVAR add-in to us and moderating this thread with helpful support.

After running the FAVAR add-in with my own dataset I came across a point that kind of surprised me. I crosschecked the FAVAR example file and found the same (potential?) issue again. Hopefully it is just me having a false understanding of the FAVAR theory, so I would really appreciate some feedback from you guys here or even from you dakila.

The point I wanna talk about relates to the irf matrix (“irfxmat”) that gets saved in the workfile when you add “save=1” to the favar command. In the standard specification of the example file the first entries in the irf matrix are always different from zero. Some are very small, yes, but they are never equal to zero. But shouldn’t the impulse responses of the slow moving variables (in the example file series16, 108, 17, 49, 50, 51, 26, 48, 118) by definition be unaffected by a shock in the federal funds rate in the first period? The irf graphs of the respective series do not start in zero but somewhere slightly above or below accordingly. I mean, the whole differentiation between slow and fast moving variables implies that the slow variables do not immediately react to shocks in the faster ones within the first period. That’s why they are slow right?

I found an EViews FAVAR tutorial by the Bank of England (https://cmi.comesa.int/wp-content/uploads/2016/03/Ole-Rummel-13-Feb-Exercise-on-factor-augmented-VARs-EMF-EAC-9-13-February-2015.pdf) and this tutorial ends up with an irf matrix in which the first entries of all slow moving variables are precisely equal to zero. To me this makes sense.

I really hope that someone out here or maybe even dakila himself could state his or her opinion on this topic. Any help would be very much appreciated.

Thanks in advance and best regards


Posts: 375
Joined: Tue Nov 24, 2015 4:57 pm

Re: FAVAR add-in

Postby dakila » Tue Oct 08, 2019 6:43 am

Hi Markus,

Here the slow and fast variables matter for recovering common components, F other than R.
In order to estimate common components F, we removes direct dependences of C(F,R) on R:
1. Estimate principal components, C(F, R) from entire dataset
2. Estimate C(F*) extracting principal components from slow moving variables
3. Run regression : C(F,R) = b1*C(F*) + b2*R
4. F = C(F,R) -b2*R

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