Dear all:

I am searching for a regression model (e.g. y=Xb+e) in which dummy-coded events 
(to time point t) on different regressors in X exhibit an effect on subsequent 
responses in the vector y (to time-points t+1, t+2,… t+n). My aim is to 
estimate how long the memory effect is and how strong a certain event 
influences subsequent responses (whether the memory decays e.g. linear or 
exponentially). My first impression is that state-space models are appropriate. 
 However, according to my first understanding in these models regularities in 
the residuals are explained without using the knowledge of the event history. 
Or is a simple linear regression model with time-shifted regressors appropriate?

The response vector y includes misses and the events in X are sampled on 
irregular time points.

Many thanks
Stefan

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