How to do ADF test on stationary series?

How to do ADF test on stationary series?

ADF Test on stationary series Now, let’s see another example of performing the test on a series of random numbers which is usually considered as stationary. Let’s use np.random.randn() to generate a randomized series.

Do you need to know the ADF test?

Since testing the stationarity of a time series is a frequently performed activity in autoregressive models, the ADF test along with KPSS test is something that you need to be fluent in when performing time series analysis. Another point to remember is the ADF test is fundamentally a statistical significance test.

How is the ADF test different from the Dickey Fuller test?

As the name suggest, the ADF test is an ‘augmented’ version of the Dickey Fuller test. The ADF test expands the Dickey-Fuller test equation to include high order regressive process in the model. If you notice, we have only added more differencing terms, while the rest of the equation remains the same. This adds more thoroughness to the test.

What does unit root mean in ADF test?

The ADF test belongs to a category of tests called ‘Unit Root Test’, which is the proper method for testing the stationarity of a time series. So what does a ‘Unit Root’ mean? Unit root is a characteristic of a time series that makes it non-stationary.

Which is an example of the ADF test?

Now, let’s see another example of performing the test on a series of random numbers which is usually considered as stationary. Let’s use np.random.randn() to generate a randomized series. The p-value is very less than the significance level of 0.05 and hence we can reject the null hypothesis and take that the series is stationary.

Is the null hypothesis rejected in the ADF test?

The null hypothesis is still rejected. adf.test () uses a model that allows an intercept and trend. Let’s try the test on a random walk (nonstationary). The null hypothesis is NOT rejected as the p-value is greater than 0.05. Try a Dickey-Fuller test. Notice that the test-statistic is larger. The p-value is greater than 0.05.