What is ADF test in time series analysis ?
Question
What is the output of ADF test
in progress
0
Machine Learning
4 years
4 Answers
1100 views
Grand Master 0
Answers ( 4 )
Before building a time series model and predicting future values, it is necessary to make a time series
stationary. By stationary , we mean that it should have constant mean and variance over time. It means
the values in a time series should not be a function of time.
There are various methods to make time series stationary like taking differences with previous lag values
and taking log.
After, we have tried any of the above methods to make the time series stationary. it is also
necessary to test if our method has been really successful in making the time series stationary.
The Augmented Dicky Fuller Test (ADF) has been really designed for the same purpose, to test the
stationarity of the time series.
It’s Null hypothesis is that the time series is not stationary and alternative hypothesis is that
the time series is stationary. So, depending upon the p-value of the test you can decide whether
the time series is stationary or not. The test operates on a 95% confidence interval, so your p-value
has to be less than 0.05 for the time series to be stationary.
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.
ADF stands for Augmented Dickey-Filler test. This test is used to check the stationarity of the time series. Since all the forecasting models require the time series to be stationary, we need checks like ADF.
In ADF the null hypothesis is that the time series is not stationary. To reject the null hypothesis, the value of Test Statistics should be below the critical values (1%, 5%, 10%). If this criteria is satisfied, we can reject the null hypothesis and can say that the series is stationary. The confidence level depends upon the value of test statistics. For example, if the value is less than 5% critical value, we can say that the time series is stationary with 95% confidence.
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. That said, it should be used with caution because—like most unit root tests—it has a relatively high Type I error rate.
The hypotheses for the test:
The null hypothesis for this test is that there is a unit root.
The alternate hypothesis differs slightly according to which equation you’re using. The basic alternate is that the time series is stationary (or trend-stationary).
Choosing Models and Lags:
Before you run an ADF test, inspect your data to figure out an appropriate regression model. For example, a nonzero mean indicates the regression will have a constant term. The three basic regression models are:
No constant, no trend: Δyt = γyt-1 + vt
Constant, no trend: Δyt = α + γyt-1 + vt
Constant and trend: Δyt = α + γyt-1 + λt + vt
The Augmented Dickey Fuller adds lagged differences to these models.
You need to choose a lag length to run the test. The lag length should be chosen so that the residuals aren’t serially correlated. You’ve got several options for choosing lags: Minimize Akaike’s information criterion (AIC) or Bayesian information criterion (BIC), or drop lags until the last lag is statistically significant.