I also want to estimate a model which captures the effect of the interaction between conditional volatility and the lagged dependent variable. Could u hint me to the code you mentioned?
The model I want to estimate in Eviews is the following:
r_t = beta_0 + beta_1 "conditional variance" + beta_2 r_t(-1) + beta_3(r_t(-1)*"conditional variance") + u_t
where the conditional variance is either modeled by an EGARCH(1,1) or TGARCH(1,1) with an GED distribution.
With the GARCH-M option in the estimation window I can include the conditional variance but not the interaction term.
I hope that your mentioned code below can help me with this problem.
trubador wrote:What Gareth means is that, you do not have to use a LogL object if you are trying to estimate a regular GARCH or GARCH-in-mean model as they are already built-in.
I wrote the code you use for another user so as to allow him/her to estimate the effect of the interaction between conditional volatility and the lagged dependent variable (i.e. garchm*y(-1)) in the mean equation. If you only need garchm in the mean equation, then the following part of the code will be enough:
Code: Select all
equation eq1.arch(1,1,ged,archm=VAR,backcast=1) y c y(-1)
There is really nothing special about the Rolling part of the model and you already have a good example.