Help required with State Space model
Posted: Fri Jun 27, 2014 5:18 am
Hello,
I am currently writing a thesis about the financial performance of Alternative Energy Indices.
To estimate the financial performance i would like to use a state-space market model combined with the Kalman Filter optimization algorithm.
The model i am using is given by the following equations:
(1) Return of index i = alpha + beta * Return of benchmark + error 1
(2) beta (t) = beta (t-1) + error 2
(3) alpha (t) = alpha (t-1) + error 3
I am using the SSpace object method for the first time and i currently came up with:
"@ename e1
@ename e2
@ename e3
@evar var(e1) = exp(C(1))
@evar var(e2) = exp(C(2))
@evar var(e3) = exp(C(3))
@signal r_argae = sv2 + sv1*r_benchmark + e1
@state sv1 = sv1(-1) + e2
@state sv2 = sv2(-1) + e3"
So far, so good ( i think ). However, the problem i have is with the second part, because i have no clue how to put this in EViews.
The second part of the equations is shown in the uploaded attachment. The likelihood function estimates the unknown parameters of the system.
If anyone could give me any tips on how to solve this, it would be very much appreciated.
Kind regards,
Vincent
I am currently writing a thesis about the financial performance of Alternative Energy Indices.
To estimate the financial performance i would like to use a state-space market model combined with the Kalman Filter optimization algorithm.
The model i am using is given by the following equations:
(1) Return of index i = alpha + beta * Return of benchmark + error 1
(2) beta (t) = beta (t-1) + error 2
(3) alpha (t) = alpha (t-1) + error 3
I am using the SSpace object method for the first time and i currently came up with:
"@ename e1
@ename e2
@ename e3
@evar var(e1) = exp(C(1))
@evar var(e2) = exp(C(2))
@evar var(e3) = exp(C(3))
@signal r_argae = sv2 + sv1*r_benchmark + e1
@state sv1 = sv1(-1) + e2
@state sv2 = sv2(-1) + e3"
So far, so good ( i think ). However, the problem i have is with the second part, because i have no clue how to put this in EViews.
The second part of the equations is shown in the uploaded attachment. The likelihood function estimates the unknown parameters of the system.
If anyone could give me any tips on how to solve this, it would be very much appreciated.
Kind regards,
Vincent