Hello all,

I am estimating a gaussian linear model

y_t+1 = X_t B + u_t

and I noticed that I can actually predict very well a transformation of y_t  
(R^2=0.82)
where yhat_t is in fact an exponential smoothed version of y_t with some 
smoothing parameter H

y_t is stationary in mean, eteroschedastic and full of outliers daily returns 
series
X_t are (optionally) smoothed values too

It is quite straightforward using weighted least squares to predict the trend 
value Et( yhat_t+1 ) but apparently and quite obviously 


y_t+1 = E(yhat_t+1)+N_t+1
N_t+1 = y_t+1 - E(yhat_t+1) 

were the noise N_t is full of jumps, autocorrelated, nearly unit root and 
eteroschedastic
 
Now my question is:

is there a way to estimate efficiently this family of models in one step and/or 
can we forecast efficiently yt+1-E(yhat_t+1) ?
Other than Crane-Crotty model that uses exponential weights?

Maybe an obvious R implementation I missed?
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