>Am 24.02.2015 um 13:53 schrieb Daniel Bencik:
>> Dear forum,
>>
>&g t; 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