- This is the case of an AR time series model where the predicted value is expected to equal the previous period plus a random error:
- When b
_{0}is not equal to zero, the model is a random walk with a drift, but the key characteristic is a b_{1}= 1. - The expected value of the error is still zero.
- The mean reverting level for a random walk is not covariance stationary and the technique of first differencing is frequently used to transform an AR model with one time lag variable (AR1) into a model that is covariance stationary.
- If an AR time series is covariance stationary, then the serial correlations for the lag variables are insignificant or they rapidly drop to zero as the number of time period lags rises.
- When the lag coefficient is not statistically different from 1, a unit root exists.
- Dickey-Fuller test = applied to AR1 model to test for a unit root.
- If a unit root is present, then the model is not covariance stationary; if this is the case, the independent variable must be transformed, so you can re-model.

x

_{t}= b_{0}+ x_{t-1 }+ ε_{t}
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