What is the purpose of adftest in the R language?

In R language, the adftest function is used for conducting unit root tests, which can determine if a time series is stationary.

Stationarity refers to the statistical property of a time series remaining constant over time, with the mean and variance staying consistent across different time periods. With a stationary time series, we can make better predictions and models.

The adftest function determines the presence of a unit root in a time series by conducting an Augmented Dickey-Fuller test. The presence of a unit root indicates lack of stationarity in the time series, while the absence of a unit root indicates stationarity.

The results returned by the adftest function include the ADF statistic, p-value, and critical values for different confidence levels. Based on the size of the p-value, we can determine if the time series is stationary. If the p-value is less than the significance level (usually 0.05), we can reject the hypothesis of a unit root and conclude that the time series is stationary. Conversely, if the p-value is greater than the significance level, we cannot reject the hypothesis of a unit root and conclude that the time series is not stationary.

In conclusion, the adftest function in R language is used to conduct unit root testing to determine whether a time series has stationarity.