The Augmented Dickey-Fuller (ADF) test is a common statistical test used to determine whether a given time series is stationary or not. Stationarity is a crucial concept in time series analysis, as many forecasting methods assume that the time series is stationary. A time series is said to be stationary if its statistical properties, such as mean and variance, remain constant over time.
In the last chapter, We have conducted ADF test on SBIN. And We had found this results –
ADF Statistic: -3.564168196716654 p-value: 0.006484183313370225
Here’s a detailed breakdown of the results obtained from the ADF test on SBIN’s closing prices:
The null hypothesis (\(H_0\)) is a specific statement made about a population parameter. The null hypothesis is often a statement of no effect or no difference.
For example, in the ADF test, the null hypothesis is that the time series has a unit root (i.e., is non-stationary).
In the context of the Augmented Dickey-Fuller (ADF) test, the null hypothesis posits that the time series data possesses a unit root, signifying that it is non-stationary.
A unit root suggests that the time series data is influenced by a stochastic trend, making it unpredictable and difficult to model.
When a time series has a unit root, it exhibits a random walk behavior, where each value in the series is a reflection of its past value plus a random shock.
The null hypothesis in the ADF test is crucial as it sets the stage for testing whether or not the time series data is stationary, which in turn impacts the appropriateness of employing certain statistical models or forecasting techniques on the data.