R help - Jun 2008 - Regresions based on Empirical transformations and varying distributions

Dear R-users,

This is partially a R question and partially general econometrics.

I have three time series which are somewhat "dissimilar": plotting
their
histograms indicates that they do not come from the same distribution, 
never mind a normal distribution.

I wish to predict the first as a function of the latter two.

I need a regression method which deals with three problems:
- I have time series data, so there is likely to be serial correlation 
between parameters (as a matter of fact, a visual look at the partial 
autocorrelation of the residuals on a simple linear regression confirms 
this)
- the variables do not come from the same distribution, i.e. their 
histograms do not look the same and they do not look "normal"
- I do not know what those distributions are

Is there a method implemented in R which:
- can deal with serially correlated data
- can deal with data that does not come from the same distribution
- can deal with not being told what those distributions are, i.e. can 
automagically decide a transformation of those variables
- provides summary data, such as the residuals, various tests of relevant 
significance hypothesis, etc.

In glm, it appears that you do need to know what the distributions are, so 
I believe that will not work, but I could be wrong.

Any pointers appreciated.

Many thanks in advance,
Tolga

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