Or is there a hidden physical reason somewhere?

(I am perhaps “beating a dead horse”, but statistics, however valuable tools it does indeed provide, does not become science without science. And, please re-direct me to some other thread/blog of this is OT here.)

Thanks,
/Johan

And I thought I was making a point about the need for a physical/rational model even when initial out-of-sample data might support a purely empirical model based on in-sample data.

Familar: as in the Team moving on from errors and the great divergence.

I am grading myself with an F for failure to get my point across. I’ll let Bender and John A grade themselves.

Yes

Sound familiar.

Lehman Bros, The Bears, or both?

They have no concept of the problems arising from initial overfitting of the model with “in-sample” data or the truer test of the model being out-of-sample.

And do they go broke as often as you’d expect?

Once the out-of-sample data indicates that the strategy is going south they move onto a new strategy. Sound familar. The biggest losses I have seen are when a model with no a prior rationalizations makes an initial big out-of-sample splash and then goes south. That out-of-sample success produces some true believers – even without a rational model for support.

Just invert it!

Here you go:
.

.
It’s upside down cake. Plus, you can have your cake and eat it too.

Memo to self: stop writing comments late at night on your iPhone. That way you won’t look stupid.

Re: Steve McIntyre (#20),

I can’t help wondering if Kaufman has missed a trick. If it was me, I’d be looking at calibrating variance with inverted temperature, the higher the variance – the lower the mean temperature. That would at least make meteorological sense since warm periods are generally periods of reduced seasonal variability, especially in the Arctic.