What's new


  1. Nicole Seaman

    P2.T2.20.24. Stationary Time Series: Box-Pierce test and model selection with AIC and BIC

    Learning objectives: Describe sample autocorrelation and partial autocorrelation. Describe the Box-Pierce Q-statistic and the Ljung-Box Q statistic. Questions: 20.24.1. The autocorrelation function (ACF) is typically paired with the partial autocorrelation function (PACF). About the ACF and...
  2. Nicole Seaman

    P1.T2.20.22. Stationary Time Series: autoregressive (AR) and moving average (MA) processes

    Learning objectives: Define and describe the properties of autoregressive (AR) processes. Define and describe the properties of moving average (MA) processes. Explain how a lag operator works. Questions: 20.22.1. Below are a set of innovations over ten steps (from initial t = 0 to t = 10) and...
  3. Nicole Seaman

    P1.T2.20.21. Stationary Time Series: covariance stationary, autocorrelation function (ACF) and white noise

    Learning objectives: Describe the requirements for a series to be covariance stationary. Define the autocovariance function and the autocorrelation function. Define white noise; describe independent white noise and normal (Gaussian) white noise. Question: 20.21.1. Pamela has been tasked to...
  4. J

    T2 - Chapter 10 Stationary Time Series Notes

    Hi @David Harper CFA FRM I am not able to understand below context. kindly help Updated by Nicole to note that this is regarding the study notes in T2 - Chapter 10 Stationary Time Series on page 13. As shown in the moving average process building equation above, the lagged shocks feed...