R help - May 2005 - Mixed-effects model with lagged endogenous variables and autocorrelated residuals

Dear all,

I am dealing with a nonlinear model of the form yt = A*exp(-B*T)*Yt-1, where T
represents time and Yt-1 accounts for the accumulated values of y from T=0 to
t-1.
The problem of the models is that the error terms are autocorrelated, so I have
to deal with a model combining autocorrelated residuals and lagged endogenous
variables.
One common approach to this specific model is the two-step Hatanaka estimator.
If I am right, it works as follows: first, the equation is fitted in order to
get an estimate of the autocorrelation term, which is then included in the
second step (the transformation is shown in Hatanaka, 1974), the new equation
being estimated by OLS. An additional problem is that the OLS estimate of the
autocorrelation term is not consistent in the presence of lagged endogenous
variables, so the use of instrumental variables is needed for the first step.
So far, so good (more or less). The question is that I am interested in fitting
a mixed-effects model (I would like to obtain subject-specific coefficients, and
my panel of data is too short to fit separate regressions), so that the
coefficients will be estimated either by ML or REML.
Do you have any clue of how to approach this? Is the rho estimate obtained from
the nlme package consistent? Could I substitute it directly in the second step?
Are there better options? Any suggestions will be MUCH appreciated.

Thank you very much,

Antonio