> Not at the moment, but addinbg this to the SVAR package has been on my to-do list for a long time. I'll see what I can do in the next few days.

If I understand correctly what you want to do, the SVAR package should make your job relatively painless: I'm attaching an example which uses the AWM dataset to set up a NK 3-equation model and estimate it under an identification strategy like the one you describe. For more details, see the SVAR documentation.

<hansl>
# load the SVAR package
include SVAR.gfn

# use the classic ECB data set
open AWM.gdt --quiet
genr time
genr dummy

# set up the data for the New Keynesian 3-eq model
series y = 100 * hpfilt(ln(YER))
series u = 100 * URX
series p = 100 * sdiff(ln(HICP))

list X = y u p
list exo = const time dq1 dq2 dq3

# find the VAR order via HQC
var 8 X ; exo --lagselect
scalar bestlag = iminc($test[,3])

# set up the model
Mod = SVAR_setup("C", X, exo, bestlag)
Mod["horizon"] = 40

#set up recursive restrictions
SVAR_restrict(&Mod, "C", 1, 2, 0)
SVAR_restrict(&Mod, "C", 1, 3, 0)
SVAR_restrict(&Mod, "C", 2, 3, 0)

#now add two extra restrictions
SVAR_restrict(&Mod, "C", 3, 1, 0)
SVAR_restrict(&Mod, "C", 3, 2, 0)

 

 

 

 

Thanks,

 

Gabriela