What does ADF mean in STATISTICS
Augmented Dickey-Fuller (ADF) is an econometric test used to statistically measure the degree of stationarity inherent within a time series dataset. Stationarity implies that the data distribution of a sequence remains the same over time. The ADF test examines the ‘null hypothesis', which states that the time series data is nonstationary, against an alternate ‘alternative hypothesis' which suggests that it is stationary.
ADF meaning in Statistics in Academic & Science
ADF mostly used in an acronym Statistics in Category Academic & Science that means Augmented Dickey-Fuller
Shorthand: ADF,
Full Form: Augmented Dickey-Fuller
For more information of "Augmented Dickey-Fuller", see the section below.
Essential Questions and Answers on Augmented Dickey-Fuller in "SCIENCE»STATISTICS"
What is the Augmented Dickey-Fuller test?
The Augmented Dicky Fuller test is a statistical process used to examine if a time series dataset has stationarity or not. It examines the null hypothesis (nonstationary) against and alternative hypothesis (stationary).
What assumptions must be met before running an ADF test?
A few assumptions must be met prior to running an ADF Test. Specifically, it assumes constant mean and variance, linearity in parameters and no autocorrelation among residuals.
What type of data does this test work for?
This test works for any type of time series datasets with observations over equal intervals such as monthly or yearly sales, stock prices, etc.
How does one interpret the output from an ADF test?
The output from an ADF Test will demonstrate how well the null hypothesis (non-stationary) fits compared to the alternative hypothesis (stationary). If p-value obtained in the results is less than 0.05 then null hypothesis can be rejected and hence we can assume that there is stationarity present in our data.
Are there any limitations to using an ADF Test?
Yes, as with any other statistical tests there are some limitations when using an ADF Test. For example, it can only assume linearity in parameters and cannot account for nonlinear variables such as outliers or trends in a dataset which may have significant influence on overall results of our analysis.
Final Words:
In conclusion, the Augmented Dickey-Fuller test is useful tool for measuring whether a certain time series dataset exhibits stationarity or not by leveraging its ability to compare two hypotheses about its behavior; Null Hypothesis and Alternative Hypothesis. This allows us to better understand how underlying factors may affect our analysis and make informed decisions about how best to proceed based on this information.
ADF also stands for: |
|
| All stands for ADF |