What does the Dickey-Fuller test tell you?
What does the Dickey-Fuller test tell you?
In statistics, the Dickey–Fuller test tests the null hypothesis that a unit root is present in an autoregressive time series model. The alternative hypothesis is different depending on which version of the test is used, but is usually stationarity or trend-stationarity.
How do you read Dickey-Fuller results?
Augmented Dickey-Fuller test
- p-value > 0.05: Fail to reject the null hypothesis (H0), the data has a unit root and is non-stationary.
- p-value <= 0.05: Reject the null hypothesis (H0), the data does not have a unit root and is stationary.
How the Augmented Dickey Fuller test is different from Dickey-Fuller test?
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.
What is ADF test used for?
Augmented Dickey Fuller test (ADF Test) is a common statistical test used to test whether a given Time series is stationary or not. It is one of the most commonly used statistical test when it comes to analyzing the stationary of a series.
Why is stationary time series important?
Stationarity is an important concept in time series analysis. Stationarity means that the statistical properties of a time series (or rather the process generating it) do not change over time. Stationarity is important because many useful analytical tools and statistical tests and models rely on it.
Which is the cointegrated augmented Dickey-Fuller test?
Cointegrated Augmented Dickey-Fuller (CADF) test determines the optimal hedge ratio by linear regression against the two stocks and then tests for stationarity of the residuals. CADF is also known as Engle-Granger two-step method. 1 2 3 4 5
Is the Dickey Fuller test a null hypothesis?
The Dickey-Fuller test statistic is very low, providing us with a low p-value. We can likely reject the null hypothesis of the presence of a unit root and conclude that we have a stationary series and hence a cointegrated pair. This is clearly not surprising given that we simulated the data to have these properties in the first place.
What do you need to know about cointegration test?
In order to perform ADF test as in last post, we need to know the hedging ratio between the two stocks. Cointegrated Augmented Dickey-Fuller (CADF) test determines the optimal hedge ratio by linear regression against the two stocks and then tests for stationarity of the residuals.
Which is the cointegration test after Engle and Granger?
This is known as the Engle-Granger Augmented Dickey-Fuller test for cointegration (or EG-ADF test) after Engle and Granger ( 1987). The critical values for this test are special as the associated null distribution is nonnormal and depends on the number of I (1) I ( 1) variables used as regressors in the first stage regression.