4 ms·

The problem is you can make forecasts but they're just guesses at best. The basic model for forecasts for example underestimates the occurrence of black swan e

The problem is you can make forecasts but they're just guesses at best. The basic model for forecasts for example underestimates the occurrence of black swan events, plain and simple. That's why these days they use the stress test approach, assume the worst market conditions then see if a bank can cope.

nl 10y ago
The problem is you can make forecasts but they're just guesses at best. This isn't true. The current US unemployment rate is 4.7%[1]. It's pretty easy to look at the time series data, and come up with a likely range for the next reading. The possibility of black swan events (as well as inadequacies of models, measurement inaccuracies etc) mean this number isn't a sure thing: instead a statistical model provides a distribution of probabilities. For example, the probability distribution curve might give an 90% chance of the unemployment rate being between 4.5% and 4.9%. If we are trying to predict a year ahead, there much less certainty. Black swan events are just one of the causes of that, but it's still possible to forecast that the unemployment rate in December 2017 has a (say) 30% chance of being between 5.5% and 4.5%. This isn't guesses (well to be clear: I'm guessing above). You model the inputs, and build a Bayesian belief network (say) to model the interactions between events. Here's a version of The Fed's model: https://www.federalreserve.gov/econresdata/notes/feds-notes/2014/a-tool-for-macroeconomic-policy-analysis.html https://www.federalreserve.gov/econresdata/notes/feds-notes/.... That's why these days they use the stress test approach, assume the worst market conditions then see if a bank can cope. That's completely different to macro-economic forecasting. [1] https://data.bls.gov/timeseries/LNS14000000 https://data.bls.gov/timeseries/LNS14000000

4 ms·

The problem is you can make forecasts but they're just guesses at best. The basic model for forecasts for example underestimates the occurrence of black swan e

by neximo64 10y ago

nl 10y ago
The problem is you can make forecasts but they're just guesses at best. This isn't true. The current US unemployment rate is 4.7%[1]. It's pretty easy to look at the time series data, and come up with a likely range for the next reading. The possibility of black swan events (as well as inadequacies of models, measurement inaccuracies etc) mean this number isn't a sure thing: instead a statistical model provides a distribution of probabilities. For example, the probability distribution curve might give an 90% chance of the unemployment rate being between 4.5% and 4.9%. If we are trying to predict a year ahead, there much less certainty. Black swan events are just one of the causes of that, but it's still possible to forecast that the unemployment rate in December 2017 has a (say) 30% chance of being between 5.5% and 4.5%. This isn't guesses (well to be clear: I'm guessing above). You model the inputs, and build a Bayesian belief network (say) to model the interactions between events. Here's a version of The Fed's model: https://www.federalreserve.gov/econresdata/notes/feds-notes/2014/a-tool-for-macroeconomic-policy-analysis.html https://www.federalreserve.gov/econresdata/notes/feds-notes/.... That's why these days they use the stress test approach, assume the worst market conditions then see if a bank can cope. That's completely different to macro-economic forecasting. [1] https://data.bls.gov/timeseries/LNS14000000 https://data.bls.gov/timeseries/LNS14000000