Hewitt pointed out that in general, you could do better than most other forecasters by favoring the status quo outcome.
I vaguely recall some academic work showing this to be true, or more generally if you’re predicting the variable X_t over time, the previous period’s value tends to be a better predictor than more complicated models. Can anyone confirm/deny my memory? And maybe provide a citation?
I vaguely recall some academic work showing this to be true, or more generally if you’re predicting the variable X_t over time, the previous period’s value tends to be a better predictor than more complicated models.
Most complicated models that I’m familiar with include both the previous value and other factors (since there is generally more going on than a random walk).
I vaguely recall some academic work showing this to be true, or more generally if you’re predicting the variable X_t over time, the previous period’s value tends to be a better predictor than more complicated models. Can anyone confirm/deny my memory? And maybe provide a citation?
This is a theme of multiple papers in the 2001 anthology Principles of Forecasting (a PDF of which is findable online), to give a specific citation.
Thanks! That’s exactly the sort of thing I was looking for, and maybe remembering.
These get called AR(1) models, for autoregressive 1.
Most complicated models that I’m familiar with include both the previous value and other factors (since there is generally more going on than a random walk).