When to use ADF and KPSS?
KPSS test is a statistical test to check for stationarity of a series around a deterministic trend. Like ADF test, the KPSS test is also commonly used to analyse the stationarity of a series. However, it has couple of key differences compared to the ADF test in function and in practical usage.
Why is Dickey Fuller augmented?
The Augmented Dickey Fuller Test (ADF) is unit root test for stationarity. Unit roots can cause unpredictable results in your time series analysis. The Augmented Dickey-Fuller test can be used with serial correlation. The ADF test can handle more complex models than the Dickey-Fuller test, and it is also more powerful.
Is the KPSS test the same as the ADF test?
A function is created to carry out the ADF test on a time series. KPSS is another test for checking the stationarity of a time series. The null and alternate hypothesis for the KPSS test are opposite that of the ADF test. Null Hypothesis: The process is trend stationary.
What are the results of the ADF test?
The ADF tests gives the following results – test statistic, p value and the critical value at 1%, 5% , and 10% confidence intervals. ADF test is now applied on the data.
Which is an alternate hypothesis for the ADF test?
Alternate Hypothesis: The series has no unit root. If the null hypothesis in failed to be rejected, this test may provide evidence that the series is non-stationary. A function is created to carry out the ADF test on a time series. KPSS is another test for checking the stationarity of a time series.
What are the results of the Dickey Fuller test?
Results of Dickey-Fuller Test: Test Statistic -2.837781 p-value 0.053076 #Lags Used 8.000000 Number of Observations Used 300.000000 Critical Value (1%) -3.452337 Critical Value (5%) -2.871223 Critical Value (10%) -2.571929 dtype: float64 Based upon the significance level of 0.05 and the p-value of ADF test, the null hypothesis can not be rejected.