New Register for Workshop on How to start for NABARD 2025 (Stepwise Plan) - Apr 26, 7 PM

    Question

    Why is testing for stationarity important in time series

    modeling, and which test is commonly used for this purpose?
    A It helps in validating data quality; Granger Causality Test Correct Answer Incorrect Answer
    B Stationary series provide constant mean and variance; Dickey-Fuller Test Correct Answer Incorrect Answer
    C Ensures seasonality is accurately captured; Augmented Linear Test Correct Answer Incorrect Answer
    D Guarantees that noise is minimized; Residual Variance Test Correct Answer Incorrect Answer
    E Confirms the series follows a Gaussian distribution; Z-Test Correct Answer Incorrect Answer

    Solution

    Stationarity is critical in time series modeling because most statistical forecasting methods assume that the series has a constant mean and variance over time. Stationary data are easier to model and predict because they lack trends and cyclical patterns that could distort analysis. The Dickey-Fuller Test, particularly its augmented version (ADF Test), is commonly used to check for stationarity by testing the null hypothesis that the series has a unit root (indicating non-stationarity). If the test rejects the null hypothesis, it indicates that the series is stationary, allowing for more reliable and robust modeling. Option A (Granger Causality Test) is incorrect as it tests causality, not stationarity. Option C (Augmented Linear Test) is incorrect because no such test exists for seasonality. Option D (Residual Variance Test) is incorrect; stationarity is not concerned with residual variance but overall series stability. Option E (Z-Test) is incorrect because it assesses differences in means, not stationarity.

    Practice Next