Mihai
> - I have seen in the function files that there is a GJR- Garch in Mean,
> is there any possibility to use that for reproducing the examples in
> Principles of Econometrics.
Yes, you may try the "GJR- Garch in Mean" either via GUI or gretl console. The estimated results are shown below.
Yi-Nung Yang
? list x = const
? garchm(r, 1, 1,x, 1, 0, 0, 0, 0.7, 0, 0, null, null)
================================================================
The coefficient of GJR Term is gamma_i
Presample variance: backcast (parameter = 0.7)
------- mean eq & variance eq in this GARCH model --------
e = y - b0
h = omega +alpha1*(e(-1))^2+gamma1*(e(-1))^2*(e(-1)<0)+beta1*h(-1)
================================================================
Using numerical derivatives
Tolerance = 1.81899e-012
Function evaluations: 69
Evaluations of gradient: 21
Model 3: ML, using observations 1-500
ll = -0.5*(log(2*pi)+log(h) + (e^2)/h)
Standard errors based on Outer Products matrix
estimate std. error z p-value
-----------------------------------------------------
b0 0.994557 0.0429156 23.17 8.19e-119 ***
omega 0.355447 0.0900577 3.947 7.92e-05 ***
alpha1 0.262756 0.0804504 3.266 0.0011 ***
gamma1 0.491780 0.204598 2.404 0.0162 **
beta1 0.287692 0.115538 2.490 0.0128 **
Log-likelihood -730.5458 Akaike criterion 1471.092
Schwarz criterion 1492.165 Hannan-Quinn 1479.361
2011/3/9 cociuba <cociuba@gmail.com>:
> Thank you for your quick response.
> -I think that it would be helpful for newbie users (like me) to find GIG
> manual much easier (like for example a link in the help window from GIG
> gui).
> - I understand, now, how to calculate TARCH model from gretl (thanks for
> the info).
> - I am using GRETL 1.9.4, build date 27.02, compiled from sources so
> it's almost up to date :-)
> - I have seen in the function files that there is a GJR- Garch in Mean,
> is there any possibility to use that for reproducing the examples in
> Principles of Econometrics.
>
> Thank you for your help, Mihai
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