Re: Panel GARCH? (Cermeno and Grier, 2006)
Posted: Mon Nov 15, 2021 10:15 pm
I've attached a cleaner version of your program that you should find easier to handle. In particular, it replaces your long lists defining the y's, yl1's and yl2's with a single list after the EQUATION instruction below, and takes care of everything from there.
You have three problems with what you're doing.
1. That's way too big a model. The panel GARCH model allows for an unrestricted N x N (symmetric) "intercept" in the GARCH recursion. With the amount of data you have, that's a 58 x 58 matrix of free parameters, which is around 1800 of them. If it ever successfully estimated (unlikely) it would probably take a couple of days.
2. From what I can see, your data are pretty much all non-stationary. I'm puzzled as to why you would estimate a common AR2 model for them rather than estimating in differences (i.e. in returns) as is standard in GARCH modeling.
3. The panel GARCH model assumes very similar GARCH dynamics among the series. A spot check of the data would indicate that that does not seem like a reasonable assumption.
Code: Select all
*
* Make a list of the endogenous variables in this estimation
*
equation endoglist *
# x2 x7 x8 x9 x10 x11 x12 x13 x14
compute n=%eqnsize(endoglist)
*
dec vect[series] y(n) u(n) yL1(n) yL2(n)
dec vect[frml] resid(n)
*
* Make 100.* copies of the data to be used
*
do i=1,n
set y(i) = 100.0*%eqnxvector(endoglist,t)(i)
end do i
*
* Create the lags of each
*
do i=1,n
set yl1(i) = y(i){1}
set yl2(i) = y(i){2}
end do i
1. That's way too big a model. The panel GARCH model allows for an unrestricted N x N (symmetric) "intercept" in the GARCH recursion. With the amount of data you have, that's a 58 x 58 matrix of free parameters, which is around 1800 of them. If it ever successfully estimated (unlikely) it would probably take a couple of days.
2. From what I can see, your data are pretty much all non-stationary. I'm puzzled as to why you would estimate a common AR2 model for them rather than estimating in differences (i.e. in returns) as is standard in GARCH modeling.
3. The panel GARCH model assumes very similar GARCH dynamics among the series. A spot check of the data would indicate that that does not seem like a reasonable assumption.