Hello,



I have completed my kalman filter problem with more details.



The transition- and the measurement equation is given by



x[t]=A[t]*x[t-1]+B[t]*epsilon[t]

y[t]=C[t]*x[t]+eta[t]



A, y, B and C are Matrices. Y[t] is the data input vector with 800 elements
(every t has one element)



My Model is described by the following
(discretisation<http://www.dict.cc/englisch-deutsch/discretisation.html>)
stochastic differential equation



Lambda[t]=lambda[t-1]+kappa*lambda[t]*delta_t+epsilon_l

R[t]=R[t-1]+mu*delta_t+epsilon_r

epsilon_l=sigma_l*sqroot(delta_t)

epsilon_r=sigma_r*sqroot(delta_t)



Ln(S[t])=lambda[t]+R[t]



The paramters for estimation are:

kappa

mu

sigma_l

sigma_r



The state-space-model for this problem is:



x[t]=(lambda[t], R[t])’

A[t]=(1-kappa+delta_t, 0; 0, 1+mu)

B[t]=(1,0;0,1)

epsilon[t]=(epsilon_l, epsilon_r)’

C[t]=(1,1)

Eta[t]=0



I used serveral alternative methods (dlm, kalmanLike, fkf, kfilter) for
parameter estimation but I don’t understand the syntax and the correct input
for model estimation.



Can anybody help me, which packed is the most best for my problem and how is
it to control?



Thanks for helping.



Best,

Thomas

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