On Sat, 19 Jul 2014, Sven Schreiber wrote:
Am 19.07.2014 21:28, schrieb Logan Kelly:
>
> Thus, I am trying the SVAR package, but the “actual” irf estimate is
> always above the bootstraped confidence bands. I am using the bias
> corrected bootstrapping method (Killian 1989).
>
>
>
> I am thinking this must be a problem with my data, not SVAR or gretl?
> Does that sound right?
>
It sounds right in the sense that I have seen these strange things
happen with other software before. IIRC the point is in the bias
correction indeed, which in some sense highlights the bias in the
original point estimate.
I bet you VAR is very close to the non-stationarity reason. Typically,
these things happen because finite sample estimates are badly biased
towards zero. Kilian's bias correction helps, but only up to a point,
since it contains a mechanism for ensuring that the bias correction does
not introduce unstable roots in the VAR, so in practice if your data are
very persistent and there's at least a near-unit eigenvalue in your
companion matrix, there's little you can do.
The script below sort-of exemplifies the issue in the univariate case (I
know it's trivial, but in fact it shows what the issue is quite clearly).
<hansl>
set echo off
set messages off
nulldata 256
phi = 0.95
# generate data
series x = 0
x = phi*x(-1) + normal()
# now estimate a "VAR"
ols x 0 x(-1) --quiet
phihat = $coeff[2] # typically, this is biased towards 0
series u = $uhat
# bootstrap!
scalar boot_rep = 199
matrix phiboot = zeros(boot_rep,1)
loop i=1..boot_rep --quiet
ub = resample(u)
series xb = 0
xb = phihat*x(-1) + ub
ols xb 0 xb(-1) --quiet
phiboot[i] = $coeff[2]
endloop
cb = quantile(phiboot, {0.05, 0.95})
printf "phihat = %g\n", phihat
printf "95%% cb = %g - %g\n", cb[1], cb[2]
</hansl>
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Riccardo (Jack) Lucchetti
Dipartimento di Scienze Economiche e Sociali (DiSES)
Università Politecnica delle Marche
(formerly known as Università di Ancona)
r.lucchetti(a)univpm.it
http://www2.econ.univpm.it/servizi/hpp/lucchetti
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