For the past four months, I've been collecting every #NFPGuesses tweet in anticipation of collecting them and analyzing them. Over the past two weeks, I've learned an entirely new statistical package (Pandas for Python) and made a very simple, yet elegant and reusable tool to quickly extract and clean my tweets.
But let's get right into it. Here's a visualization of everyone's guesses from last month. I put myself in red:
Surprisingly, there are fewer extreme optimists/pessimists than I expected. This is likely because I filtered out accounts with fewer than 100 followers, but most of the outliers are either jokes:
Twitter seems to be in-line with industry expectations, but when there's a miss, everybody misses. Last month, industry and twitter consensus was well above average– March was an awful hiring month!
It looks like Twitter is as good a forecaster as Wall Street. But this shouldn't surprise anyone, because macroeconomic forecasting is hard, NFP data is noisy (look at the revisions), and #NFPGuesses is "obscure" enough that only people who are already somewhat economically inclined will participate.
Stay tuned, because I'm going to do a lot more with this data... people should be turning in their guesses for tomorrow's numbers right now, and I've practically done all the heavy lifting already!
And oh, right...
But let's get right into it. Here's a visualization of everyone's guesses from last month. I put myself in red:
Surprisingly, there are fewer extreme optimists/pessimists than I expected. This is likely because I filtered out accounts with fewer than 100 followers, but most of the outliers are either jokes:
i am going to stick with my usual low ball on #NFPguesses and go with 134, and thats not short hand for 134k, just 134
— Ryan Longhenry (@Six1FourCapital) May 8, 2015
I see your #NFPguesses and raise you: 666 https://t.co/5pGfFVLjcD
— MARK GILBERT (@ScouseView) May 8, 2015
or mismatched entries (human data entry would be more reliable, but python is faster!).Twitter seems to be in-line with industry expectations, but when there's a miss, everybody misses. Last month, industry and twitter consensus was well above average– March was an awful hiring month!
It looks like Twitter is as good a forecaster as Wall Street. But this shouldn't surprise anyone, because macroeconomic forecasting is hard, NFP data is noisy (look at the revisions), and #NFPGuesses is "obscure" enough that only people who are already somewhat economically inclined will participate.
Stay tuned, because I'm going to do a lot more with this data... people should be turning in their guesses for tomorrow's numbers right now, and I've practically done all the heavy lifting already!
And oh, right...
+200k #NFPGuesses
— Roger Filmyer (@rfilmyer) June 4, 2015
See you soon!
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