Which test is commonly applied in time series analysis to determine whether a series is stationary?

The Dickey-Fuller Test is a widely used statistical test in time series analysis to evaluate if a series is stationary. It tests the null hypothesis that a unit root is present in the series, indicating non-stationarity. If the test statistic is significantly lower than a threshold (indicating a low p-value), the null hypothesis is rejected, suggesting the data is stationary. Achieving stationarity is crucial for time series models like ARIMA, which assume stable mean and variance over time. Therefore, the Dickey-Fuller Test is essential for determining whether data requires differencing or other transformations before analysis. The other options are incorrect because: • Option 1 (Shapiro-Wilk Test) checks for normality, not stationarity. • Option 3 (Chi-Square Test) evaluates relationships between categorical variables. • Option 4 (Jarque-Bera Test) tests normality in large samples, not stationarity. • Option 5 (ANOVA) assesses mean differences across groups, unrelated to stationarity.