Say what mean reversion is and work out the level a series tends to revert to
The idea
A series is mean reverting if it tends to fall when above its mean and rise when below it. For an AR(1) model with b1 below 1 in absolute value, the mean-reverting level is b0 ÷ (1 − b1): at that value the model predicts no change, because x = b0 + b1x. If the current value is above it, forecasts decline towards it; if below, they rise. Every covariance-stationary series has a finite mean-reverting level. A random walk has none, because its b1 is 1 and the formula divides by zero. Common errors are dividing b0 by b1 or by 1 + b1, and treating the level as next period's forecast rather than a long-run anchor.
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