Tuesday, June 19, 2018

Q: When is bad methodology fine?

A: When you clearly say it's bad methodology.

For example, I am currently playing around with housing starts data and the dynamic information equilibrium model (DIEM). It really only looks like the data from about 1990 on can be described by the model (which interestingly matches up with a similar situation with the ratio of consumption to investment).

However, I noticed something in the data -- if you delete the leading edges of recessions, the DIEM works further back. It's possible that a step response is involved; here's the log-linear transform of the data:

It's totally bad methodology to just willy-nilly delete segments of data by eye, and I wouldn't create a forecast with this model result that I'd take seriously. I won't even transform back to the original data representation to help prevent this graph from being used for other purposes. But sometimes I notice prima facie interesting things, and as this blog effectively operates as my lab notebook [1] I try to document them. They could turn out to be nothing! Why? Bad methodology!


[1] There's apparently an "open notebook" movement that I guess I've been a part of since 2013.

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