for monthly time series dataset what should my adf test type be in r if i dont know whether my data is stationary or not in r

If you are unsure whether your time series data is stationary or not, you can use the Augmented Dickey-Fuller (ADF) test in R to determine the stationarity of the data.

The ADF test is commonly used to test for the presence of a unit root in the time series data. The presence of a unit root indicates that the time series is non-stationary. The ADF test can help determine whether differencing is required to make the time series stationary.

To perform the ADF test in R, you can use the adf.test() function from the tseries package. Here's an example:

main.r
library(tseries)

# Assuming your monthly time series data is stored in a variable called 'ts_data'
adf_result <- adf.test(ts_data)

# Extract the p-value from the ADF test result
p_value <- adf_result$p.value

# Check the p-value to determine stationarity
if (p_value < 0.05) {
  print("The data is stationary.")
} else {
  print("The data is non-stationary.")
}
364 chars
15 lines

In the code above, the adf.test() function is used to perform the ADF test on the ts_data. The p-value from the test result is then extracted and compared to a significance level of 0.05. If the p-value is less than 0.05, it suggests that the data is stationary; otherwise, it suggests that the data is non-stationary.

Make sure to install and load the tseries package before running the code:

main.r
install.packages("tseries")
library(tseries)
45 chars
3 lines

Keep in mind that the ADF test is not a definitive test for stationarity, and it is always recommended to inspect the data visually and explore other methods to confirm the stationarity of the time series.