Chapter 10. Stationary Time Series Study Notes contains 28 pages covering the following 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.
* 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.
* Explain mean reversion and calculate a mean-reverting level.
* Define and describe the properties of autoregressive moving average (ARMA) processes.
* Describe the application of AR, MA, and ARMA processes.
* Describe sample autocorrelation and partial autocorrelation.
* Describe the Box-Pierce Q-statistic and the Ljung-Box Q statistic.
* Explain how forecasts are generated from ARMA models.
* Describe the role of mean reversion in long-horizon forecasts.
* Explain how seasonality is modeled in a covariance-stationary ARMA.
After reviewing these notes you will be able to apply what you learned with practice questions.Shop Courses