Hello all, I'm trying to find out how to perform a 'segmented regression'. I have some data and the 'classical' model used is: y(t) = a + bx(t) + cx(t)^2 + u(t) for x(t) < x(0) y(t) = a + bx(0) + cx(0)^2 + d(x(t) - x(0)) + u(t) for x(t) > x(0) and u(t) = rho.u(t-1) + eps (It's a model using an ARIMA-model, in this case AR(1)-process) The parameters to estimate here are: a, b, c, d and x(0) (with the last one, the most important one). u(t) is estimated using the rho of the AR(1). How can I use such a 'segmented formula' or 'segmented model' in R or how do I perform such a regression (nonlinear or restricted maximum likelihood)? thanks in advance, Kurt
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