Thanks for the response. Actually, I want b(W) to be a non-linear
function with the range restricted to (0,1). Also, I don't want to specify its
functional form and
I am more interested in non-parametric estimation.
I have not seen this type of models in Davidson and MacKinnon...
I am going to check random coefficients literature.
On Wed, 21 Mar 2001, Alexander Tsyplakov wrote:
> If b(W) is linear then the model is just a linear
> regression. If b(W) is linear and includes error term then
> the model is a linear regression with random coefficients.
> Regression with random coefficient is a linear regression
> with heteroskedasticity of known form and could be estimated
> using maximum likelihood method or generalized method of
> moments.
>
> Very useful treatment of linear/nonlinear regression,
> regression with heteroskedasticity, maximum likelihood and
> generalized method of moments could be found in
> R. Davidson & J.MacKinnon, Estimation and Inferense in
> Econometrics
>
> -----------------------------
> Alexander Tsyplakov
> Novosibirsk State University
> http://www.nsu.ru/ef/tsy/
>
> Vadim Marmer wrote...
> > Does anybody know if there is literature that discusses
> the following type
> > of regression models:
> >
> > Y=X*b(W)+u
> >
> > i.e. linear regression, but coefficients are a function
> (possibly random)
> > of some variable W. I am interested in estimation
> (parametric or
> > non-parametric) of this kind of model.
>
>
>
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