The Olympic spirit for forecasting
The Olympics finally finished, and the Paralympics are about to begin. I managed to go to some of the Olympic football matches in both Lyon (in the photo above) and Nice. The vibe in general was very good, and you could see there was a lot excitement about the Olympics in both cities. Thinking back to the Olympics in London, there's always a bit of scepticism in the run up, but as soon as the event starts, a sudden wave of enthusiasm appears, and I suspect it was similar in the case of Paris 2024.
There was some poll going around on Twitter (how scientific it was, I have no idea), which seemed to suggest that most folks felt they had a good chanc e of being to compete at the next Olympics. I suspect that the only thing this proves is that the set of folks who think they will be able to compete at the Olympics and the set of those who misjudge probabilities overlaps substantially. Joking aside, to compete and in particular to win an Olympic event is exceptionally difficult, and the margins of victory can be very small. The time of the 100 metres men's final winner, Noah Lyles, was 9.79 seconds and the last placed finalist, had a time of 9.91 seconds (see World Athletics).
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When it comes to financial markets, to be right the margins to be "right" can vary. If you take a release such as US CPI, the equivalent maybe of the 100 metres final (!), if we collect the forecasts submitted to Bloomberg's survey few days beforehand, the mean absolute error can be under 10bps (for the year on year number). To be closer than the consensus is difficult. There are lots of forecasters participating in the survey. Also by the time US CPI is released, it will be after the reference month has passed. For example, this month the US CPI for reference month of July was released on 14 August. Hence, it becomes more a matter of measuring (ie. nowcasting) what has happened to prices over the previous month, as opposed to forecasting. Just a few days before the release, the competition for getting this number is intense and furthermore there's less uncertainty because we "know"...