[R] Regression Model with Memory

[Stefan Koenig]

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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