That’s just not true. We literally do bank contagion simulations and there’s a ton of research on this topic. Prevailing economic thinking is precisely the opposite of what you said.

So I think those takeaways are a bit difficult to see in this plot. I can see a very modest increase in wellbeing but it’s hard to know if that increase is even statistically significant. The wide distribution across all incomes is interesting but unsurprising of course in every income bracket there are happy and unhappy people. It’s also hard to accurately compare the income brackets because it looks as though the scale for the violin is the same. Ie we would expect the highest income to be “thin” because it’s the smallest population. Standardizing this would let us better see the distributions in a comparable way. I’m not sure if you even have that information available just a comment generally on the plot because the story is hard to pull out from even a moderate viewing.

How does this chart show us anything related to the outcomes of that study and what exactly are you expecting people to be able to see from this chart?

Hard to identify much for major patterns in this but it may be because you have overlapping color labels making some of the industries indistinguishable on the chart.

So with no other info on your methodology I can’t think of any issue with this. In some sense you’re RNN may be modeling the trend component and the other model measuring the volatility. But that’s hard to say not knowing any more. I am curious if you tried stacking the models directly such that the weights optimize through both models simultaneously. But that depends what kind of models you have and isn’t necessarily better just different.

Am I understanding correctly that you train one model from input features to output minimizing the error to the true output then take the predictions of this first model and feed it into the RNN with other features and again minimize the loss to the true output?

… that’s the 2019 suicide rate and the 2018 life expectancy correlated with those election results. But yeah good call my point is the one that doesn’t make sense

I have no dog in this politics fight but OP did use data for both suicides and life expectancy from prior to the pandemic. So your point doesn’t make sense.

I agree, definitely need to control for the overall nationwide crime spike. On top of that this chart seems to show some increasing trend as it is though it’s hard to tell with the time axis being relatively short compared to the size of the increase in murders.

That’s an overly simplistic perspective. Crime spiked in most jurisdictions regardless of whether the government changed hands. I don’t know definitively if there is statistical evidence politics had some effect but distilling the crime spike to a single factor like this simply inaccurate.

This is really only meaningful if the google trends statistics are a leading indicator. Perhaps a Granger Causality test would best express if there is a relationship here.

I’m working on an interface where you can upload your own data now!

Data comes from my bank transactions, google chrome web history, gmail email data (both accessed through Google Takeout), as well as responses to my daily Whoop (fitness tracker) journal. Compiled and analyzed in Python. Visualizations using Plotly. Insights developed through several combined ML approaches.