I am using the Kalman filter implementation (mxExpectationStateSpace) to fit a time-discrete vector auto-regressive model to single subjects' multivariate time series. If I'm not mistaken, the Kalman filter generates (predicted and updated) process state estimates for each but the first time point. Is it possible to pull these state estimates out online while the data are being filtered?
This would seem to provide the opportunity to include interactions between the (latent) processes over time. Currently, I am allowing the parameters of my bivariate process model (e.g., the auto- and cross-regressive effects) to be moderated by a third observed time-varying variable which I include as fixed. Therefore, temporal dependencies in this third variable, measurement error etc. cannot be accounted for. It would be much more convenient if it could be treated as a (latent) random variable.
Thanks, Janne
PS: Looking forward to the implementation of thresholds for discrete data!