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Unit root test
Time series statistical test

In statistics, a unit root test tests whether a time series variable is non-stationary and possesses a unit root. The null hypothesis is generally defined as the presence of a unit root and the alternative hypothesis is either stationarity, trend stationarity or explosive root depending on the test used.

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General approach

In general, the approach to unit root testing implicitly assumes that the time series to be tested [ y t ] t = 1 T {\displaystyle [y_{t}]_{t=1}^{T}} can be written as,

y t = D t + z t + ε t {\displaystyle y_{t}=D_{t}+z_{t}+\varepsilon _{t}}

where,

  • D t {\displaystyle D_{t}} is the deterministic component (trend, seasonal component, etc.)
  • z t {\displaystyle z_{t}} is the stochastic component.
  • ε t {\displaystyle \varepsilon _{t}} is the stationary error process.

The task of the test is to determine whether the stochastic component contains a unit root or is stationary.1

Main tests

Other popular tests include:

Unit root tests are closely linked to serial correlation tests. However, while all processes with a unit root will exhibit serial correlation, not all serially correlated time series will have a unit root. Popular serial correlation tests include:

Notes

References

  1. Kočenda, Evžen; Alexandr, Černý (2014), Elements of Time Series Econometrics: An Applied Approach, Karolinum Press, p. 66, ISBN 978-80-246-2315-3. 978-80-246-2315-3

  2. Dickey, D. A.; Fuller, W. A. (1979). "Distribution of the estimators for autoregressive time series with a unit root". Journal of the American Statistical Association. 74 (366a): 427–431. doi:10.1080/01621459.1979.10482531. /wiki/Journal_of_the_American_Statistical_Association