State space estimation
Posted: Tue May 06, 2014 1:05 am
Hi, could somebody please help me clarify this point? Thank you in advance for the help.
I am estimating a state space model of this kind:
@SIGNAL D(log(prod)) = SV1 + c(1)*Gap + [VAR=EXP(C(2))]
@STATE SV1 = c(3)*SV1(-1) + [VAR=EXP(C(4))]
c(1) is a fixed regression coefficient. How is it estimated c(1) in State Space framework? Is it obtained via Maximum likelihood (ML) or OLS?
I think that only unobservable components and variances are estimated via ML, because if I run an OLS of this kind:
D(log(prod)) = sv1 + c(1)*Gap,
c(1) in OLS and in state space are equal. Thank you.
I am estimating a state space model of this kind:
@SIGNAL D(log(prod)) = SV1 + c(1)*Gap + [VAR=EXP(C(2))]
@STATE SV1 = c(3)*SV1(-1) + [VAR=EXP(C(4))]
c(1) is a fixed regression coefficient. How is it estimated c(1) in State Space framework? Is it obtained via Maximum likelihood (ML) or OLS?
I think that only unobservable components and variances are estimated via ML, because if I run an OLS of this kind:
D(log(prod)) = sv1 + c(1)*Gap,
c(1) in OLS and in state space are equal. Thank you.