Am 24.02.2015 um 13:53 schrieb Daniel Bencik:
> Dear forum,
>
> this is more of a question related to econometrics. When, for
> example, your goal is to model/forecast weekly highs/weekly lows,
> when you run your regression on Tuesdays, you already know that the
> model should not predict a weekly high below the Monday's high. The
> Wednesday's prediction of the whole week's high should not be below
> max(mondayHigh, tuesdayHigh). My questions is whether there is an
> econometric tool/approach that is capable of estimating a model
> bearing this in mind. That is, I want the estimated coefficients to
> take into account, that the forecasts should not be below/above some
> value which changes over time (i.e. it is not a constant like e.g.
> zero or something).
>
If I understand your question correctly, there is a trivial solution, 
although you may not like it: Produce forecasts with standard tools, and 
then apply your time-varying max() operator.
Or you could specify your model in terms of squared deviations (or the 
negative of that) centered on your previous high, and your restriction 
would hold. I'm not sure that makes much sense, but econometrically it's 
not a big deal unless you also want to have some other optimality 
properties.
cheers,
sven 
 
 
Sven, 
 
thank you much. I thought about the second idea. So instead of
 
weeklyHigh[t] = f(....) + a*monHigh[t] + eps
 
I should regress 
 
weeklyHigh[t] - monHigh[t] = g(...) + eps2
 
where eps2 is a positive distributed error, right? I am asking beucase this still poses
some issues and mostly does not guarantee that the predicted  weeklyHigh[t] - monHigh[t]
< 0.
 
Thank you, 
Daniel