I thought I'd put together a collection of some of the dynamic information equilibrium models (DIEMs) that only went out as tweets over the past couple weeks.
I looked at life expectancy in the US and UK (for all these, click to enlarge):
The US graph appears to show discrete shocks for antibiotics in the 40s & 50s, seatbelts in the 70s, airbags in the 90s & 2000s along with a negative shock for the opioid crisis. At least those are my best guesses! In the UK, there's the English Civil War (~ 1650s) and the British agricultural revolution (late 1700s). Again — my best guess.
Another long term data series is share prices in the UK:
Riffing on a tweet from Sri Thiruvadanthai I made this DIEM for truck tonnage data — it shows the two phases of the Great Recession in the US (housing bubble bursting and the financial crisis):
There's also PCE and PI (personal consumption expenditures and personal income). What's interesting is that the TCJA shows up in PCE but not PI — though that's likely due to the latter being a noisier series.
Here's a zoom in on the past few years:
Bitcoin continues to be something well-described by a DIEM, but with so many shocks it's difficult to forecast with the model:
We basically fail the sparseness requirement necessary to resolve the different shocks — the logistic function stair-step fails to be an actual stair-step:
A way to think about this is that the slope of this time series (the "shocks") are a bunch of Gaussians. When they get too close to each other and overlap, it's hard to resolve the individual shocks.
That's all for now, but I might update this with additional graphs as I make them — I'm in the process of a terrible cold and distracting myself with fitting the various time series I come across.