Hello, I would like use Kalman filter for estimating parameters of a stochastic model. I have developed the state space model but I don’t know the correct way use Kalman filter for parameter estimation. Has anybody experience in work with Kalman filter in R. I don’t know the correct function. Maybe it is - KalmanLike; but what is the correct Input? - tsmooth? - kfilter? Thanks for helping. I have ask the same question in the help list “sig-dynamic-models” Best, Thomas [[alternative HTML version deleted]]
Hi, There are a few packages that I would suggest to run Kalman filter. Take a look at dlm and KFAS. If you need more help you should be more precise in formulating your problem, providing a small example, as required by the posting guide. Best, Giovanni Petris Quoting Garten Stuhl <gartenstuhl2 at googlemail.com>:> Hello, > > > > I would like use Kalman filter for estimating parameters of a stochastic > model. I have developed the state space model but I don?t know the correct > way use Kalman filter for parameter estimation. Has anybody experience in > work with Kalman filter in R. > > > > I don?t know the correct function. Maybe it is > > > > - KalmanLike; but what is the correct Input? > > - tsmooth? > > - kfilter? > > > > Thanks for helping. > > > > I have ask the same question in the help list ?sig-dynamic-models? > > > > Best, > > Thomas > > [[alternative HTML version deleted]] > >
Hello, thanks for answer my Question. I prefer use KalmanLike(y, mod, nit = 0, fast=TRUE). For parameter estimating I have a given time series. In these are several components: Season and noise; furthermore it gives a mean reversion process. The season is modelled as a fourierpolynom. From the given time series I have to estimate the - Season parameters - The mean reversion factor - variance from the noise I think in the function KalmanLike y is the vector of the time series; what does "mod" mean? How can I write the syntax for the state space? Have anybody a simple example for better understanding KalmanLike. Or is it better to use other packages for parameter estimating? I have no experience in work with Kalman filters and I'm a new R user. Thanks for helping. Best, Thomas [[alternative HTML version deleted]]
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 [[alternative HTML version deleted]]
Federico, as far as I understand Kalman filter works under gaussian conditions, and for this reason it is not implemented. (I have to admit that I do not know the sspir package) hope this helps, and correct me if I am wrong Best regards Stefano Sofia PhD On 11/17/2010 11:49 AM, feder wrote:> Hi, > I used sspir for managing non-gaussian State space models but I observed > that for such models only the smoother is gave while the filter is missing. > Why?AVVISO IMPORTANTE: Questo messaggio di posta elettronica pu? contenere informazioni confidenziali, pertanto ? destinato solo a persone autorizzate alla ricezione. I messaggi di posta elettronica per i client di Regione Marche possono contenere informazioni confidenziali e con privilegi legali. Se non si ? il destinatario specificato, non leggere, copiare, inoltrare o archiviare questo messaggio. Se si ? ricevuto questo messaggio per errore, inoltrarlo al mittente ed eliminarlo completamente dal sistema del proprio computer. Ai sensi dell'art. 6 della DGR n. 1394/2008 si segnala che, in caso di necessit? ed urgenza, la risposta al presente messaggio di posta elettronica pu? essere visionata da persone estranee al destinatario. IMPORTANT NOTICE: This e-mail message is intended to be received only by persons entitled to receive the confidential information it may contain. E-mail messages to clients of Regione Marche may contain information that is confidential and legally privileged. Please do not read, copy, forward, or store this message unless you are an intended recipient of it. If you have received this message in error, please forward it to the sender and delete it completely from your computer system.