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This is a cross-post from here

In my question on a load forecast model using temperature data as covariates I was advised to use regression splines. This really seems to be a/the solution.

Now I face the following problem: if I calibrate my model on winter data (for technical reasons calibration can not be done on a daily basis, rather every second month) and slowly spring arises I will have temperature data outside of the calibration set.

Although load forecasting is not a financial topic per se the same issues arise in quantitative finance at certain points.

Are there good techniques to make the regression spline fit robust for values outside of the calibration range? The members of cross-validated already proposed natural splines where extrapolation is done linear. What do you do if you have to do something? And please don't hate me for this question - I am aware that anything outside of the training set is bad but what is a good solution if I have to do something ... ? Any experiences or references? Thanks!

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There seems to be answers your link. You aren't satisfied with them? This topic is more relevant over there. –  John Jan 31 '13 at 17:26
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I added an answer/link to your original CrossValidated question. –  bill_080 Jan 31 '13 at 20:28
    
@John I have the feeling that some questions are relevant and interesting for QSE as well as StatsSE - I am thinking of regression and similar topics. However these problems are probably treated differently here and there. So I am/was interested in some more views here. I hope that some more comments come in. Coming back: I think in quantitative finance a regression model in some kind is often applied and then my question can easily arise. –  Richard Feb 1 '13 at 11:14
    
@bill_080 Thanks for the link, I had a quick look and I will go through it in detail soon. –  Richard Feb 1 '13 at 11:15

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