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Notes -
"Trump fires Bureau of Labor Statistics chief without evidence for political reasons" says the news radio I wake up to, then continues to say he removed the Democrat appointee "without concrete evidence." Since COVID-19 caused lockdowns, the BLS numbers have been revised downward from initial reports regularly, sometimes ridiculously so, which Axios says has justifiable reasons.
So why are the initial numbers even reported if we know the algorithm they use will be wildly inaccurate?
This seems like focusing on the wrong part of the story.
"Moreover, the BLS and other federal agencies are essentially trying to serve two purposes at once. On the one hand, they’re hoping to provide actionable information as fast as they reasonably can to employers, investors, job-seekers, policymakers and the Federal Reserve. On the other hand, they’re trying to formulate a “permanent record”, data that is treated as ground truth in future economic analysis. Those imply taking different positions in the unavoidable trade-off between speed and precision."
Biased estimators can still be useful. If you know an estimator is consistently high, you can account for that in your planning. On the other hand, if political leadership is putting their thumb on the scale to make themselves look good (or salve dear leader's ego), trustworthiness goes out the window. It's one thing to be wrong occasionally, it's another to be bullshit.
If the estimator knows that they're consistently high, why aren't they adjusting the model they're using to produce estimates with to account for that?
If the estimator is wrong consistently but in a predictable way... they should be able to be wrong less often?
When I say "account for that in planning", I don't mean you adjust your forecasts downward X% from the report because they always overestimate by the same margin. Consistently high is not the same thing as 'always high' or 'consistently high by the same amount'. It just means that on average the estimator is greater than the true value (or, really, the quick estimate tends to be higher than the slow estimate).
Not necessarily. Estimation is always dealing with real world constraints liked limited resources and time frame for gathering and analyzing data, sampling bias, unknown unknowns, etc...
I encourage you to read the Nate Silver article I linked. He talks about this significantly more articulately than I can.
I am somewhat familiar with Nate Silver's approach to modelling and prediction.
And I'll reiterate the general critique.
If you damn well know your model is going to be inaccurate, include error bars, express how much irreducible uncertainty there is. At least acknowledge that the number is most likely incorrect and is subject to large revisions, downplay confidence.
Actually, it looks like they DO have that option on display and HOLY CRAP the bars are really large on some of these.
Maybe its not a particularly useful estimate if businesses are looking for something something reliable to act upon.
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