Dear R-brains, I'm rather new to state-space models and would benefit from the extra confidence in using the excellent package sspir.
In a one-factor model, If I am trying to do a simple regression where I assume the intercept is constant and the 'Beta' is changing, how do I do that? How do i Initialize the filter (i.e. what is appropriate to set m0, and C0 for the example below)? The model I want is: y = alpha + beta + err1; beta_(t+1) = beta_t + err2 I thought of the following: library(mvtnorm) # (1) library(sspir) # Let's get some data so we can all try this at home dfrm <- data.frame( y = c(0.02,0.04,-0.03,0.02,0,0.01,0.04,0.03,-0.01,0.04,-0.01,0.05,0.04, 0.03,0.01,-0.01,-0.01,-0.03,0.02,-0.04,-0.05,-0.02,-0.04,0,0.02,0, -0.01,-0.01,0.01,0.09,0.03,0.03,0.05,0.04,-0.01,0.05,0.03,0.01, 0.04,0.01,-0.01,-0.02,-0.01,-0.01, 0.06,0.03,0.02,0.03,0.03,0.04, 0.03,0.04,-0.02,-0.03,0.04,0.03,0.05,0.02,0.03,-0.1), x = c(-0.03,-0.01,0.07,-0.03,-0.07,0.05,0.02,-0.05,-0.04, -0.02,-0.19,0.07,0.09,0.01,0.01,0,0.05,0,-0.02,-0.09, -0.12,-0.01,-0.13,0.04,0.04,-0.07,-0.05,-0.03, -0.01,0.11,0.06,0.03,0.06,0.06,-0.01,0.07,0.01, 0,0.07,0.04,-0.02,0,-0.03,0.04,-0.04,-0.01,0.03,0.02,0.05,0.04, 0.05,0.03,0,-0.04,0.05,0.05,0.06,0.02,0.04,-0.06) ) ss <- ssm(y ~ tvar(x), time = 1:nrow(dfrm), family=gaussian(link="identity"), data=dfrm) smooth.params <- smoother(kfilter(ss$ss))$m (1) I read in http://ww.math.aau.dk/~mbn/Teaching/MarkovE05/Lecture3.pdf that this is requred as there is a bug in sspir. To what should I set ss$ss$m0 and ss$ss$C0? (I did notice that smoother() replaces these, but it still matters what I initialize it to in the first place) Many thanks! Tariq Khan ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html