Augmented Dickey-Fuller Test in R (With Example)

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A time series is said to be “stationary” if it has no trend, exhibits constant variance over time, and has a constant autocorrelation structure over time.

One way to test whether a time series is stationary is to perform an augmented Dickey-Fuller test, which uses the following null and alternative hypotheses:

H₀: The time series is non-stationary. In other words, it has some time-dependent structure and does not have constant variance over time.

H_A: The time series is stationary.

If the p-value from the test is less than some significance level (e.g. α = .05), then we can reject the null hypothesis and conclude that the time series is stationary.

The following step-by-step example shows how to perform an augmented Dickey-Fuller test in R for a given time series.

Example: Augmented Dickey-Fuller Test in R

Suppose we have the following time series data in R:

data 

Before we perform an augmented Dickey-Fuller test on the data, we can create a quick plot to visualize the data:

plot(data, type='l')

To perform an augmented Dickey-Fuller test, we can use the adf.test() function from the tseries library. 

The following code shows how to use this function:

library(tseries)

#perform augmented Dickey-Fuller test 
adf.test(data)

	Augmented Dickey-Fuller Test

data:  data
Dickey-Fuller = -2.2048, Lag order = 2, p-value = 0.4943
alternative hypothesis: stationary

Here’s how to interpret the most important values in the output:

• Test statistic: -2.2048
• P-value: 0.4943

Since the p-value is not less than .05, we fail to reject the null hypothesis.

This means the time series is non-stationary. In other words, it has some time-dependent structure and does not have constant variance over time.

Additional Resources

How to Perform a Mann-Kendall Trend Test in R
How to Plot a Time Series in R
How to Detrend Data