[R] Autocorrelation in non-linear regression model

[pruro]

Hey all!

I am working on my master thesis and I am desperate with my model. 
It looks as following:

Y(t) = β1*X1(t) + β2*X2(t) + δ*(β1*((1+c)/(δ+c))+β2)*IE(t) -
β2*α*((1+c)/(δ+c))*(δ+g)* IE(t-1)

note: c and g is a constant value

The problem I encounter is that between IE(t) and IE(t-1) there is strong
linear correlation (autocorrelation). How can I solve this problem? Of
utterly importance is to have finally a significant coefficient δ and α
which is than used for a consecutive model. However, I get either no
significant values for δ and α, or for one of the two some unrealistic
values.

Is there an option to combine both in using some non-linear time lagged
model, time series or plugged in autoregression? 

A following up question would be how to place penalties for this model. I
would like to restrict values for δ and α between 0 and 0.5 and add
penalties when they come closer to the boundaries.

I really need some help. Because I am stuck with it for the last two weeks
and don't know how to go about it.

Thanks for the support

Cheers,

Bob


--
View this message in context: http://r.789695.n4.nabble.com/Autocorrelation-in-non-linear-regression-model-tp3385647p3385647.html
Sent from the R help mailing list archive at Nabble.com.