So I do not find example what I expect. I plan to estimate the multi-factor model for Kalman Filter Mean Reverting, Random Walk and Random Coefficient.
For example: R(it)= Alpha(it)+ Beta(it)R(mt)+Gamma(it)(R(mt)^2)+delta(it)(R(mt)^3)+ V(it) KF Random walk Alpha(it)= Alpha(it-1)+W(i1t) Beta(it)= Beta(it-1)+W(i2t) Gamma(it)= Gamma(it-1)+W(i3t) Delta(it)= Delta(it-1)+W(i4t) Note: (alphabar= Mean Alpha, Betabar= Mean Beta, Gamma= Mean Gamma, Deltabar= Delta Mean) KF Mean Reverting Alpha(it)= Alphabar(i)+ phi* (Alpha(it-1)-Alphabar(i))+W(i1t) Beta(it)= Betabar(i)+ phi* (Beta(it-1)-Betahabar(i))+W(i2t) Gamma(it)= Gammabar(i)+ phi* (Gamma(it-1)-Gammabar(i))+W(i3t) Delta(it)= Deltabar(i)+ phi* (Delta(it-1)-Deltabar(i))+W(i4t) Kf Random Coefficient Alpha(it)= Alpha bar(i)+ W(i1t) Beta(it)= Beta bar(i)+ W(i2t) Gamma(it)= Gamma bar(i)+W(i3t) Delta(it)= Deltabar(i)+W(i4t) Step 1) Maximize MLE to estimate initial values (etc: Alphabar, ...., Delta bar, Variances of State equation Error, Observation Error,..... etc... ) ( I also use L-BFGS-B methods to optimization but I failed. :( ) Step 2) Apply estimated values from step 1 in Kalman Filter to filtering. Then obtain MSE etc ( I can calculate by myself) Please let me know whether I can follow these steps in DLM package or not. Regards, Ser -- View this message in context: http://r.789695.n4.nabble.com/State-Space-Kalman-Filter-tp4646615p4646675.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
