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.

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

${\displaystyle y_{t}=D_{t}+z_{t}+\varepsilon _{t}}$

where,

• ${\displaystyle D_{t}}$ is the deterministic component (trend, seasonal component, etc.)
• ${\displaystyle z_{t}}$ is the stochastic component.
• ${\displaystyle \varepsilon _{t}}$ 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]

Other popular tests include:

• augmented Dickey–Fuller test^[2]

  this is valid in large samples.

• Phillips–Perron test
• KPSS test

  here the null hypothesis is trend stationarity rather than the presence of a unit root.

• ADF-GLS test

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:

• Breusch–Godfrey test
• Ljung–Box test
• Durbin–Watson test