Instructional Video: Stationary Time Series

Diebold, Chapters 7 and 8 is a 20 minute instructional video analyzing the following concepts:

Chapter 7: Characterizing Cycles

* Define covariance stationary, autocovariance & autocorrelation function, partial autocorrelation function and autoregression
* Describe the requirements for a series to be covariance stationary
* Define white noise, describe independent white noise and normal (Gaussian) white noise
* Explain the characteristics of the dynamic structure of white noise
* Explain how a lag operator works.
* Describe Wold’stheorem. Define a general linear process. Relate rational distributed lags to Wold’stheorem
* Calculate the sample mean and sample autocorrelation, and describe the Box-Pierce Qstatisticand the Ljung-Box Q-statistic.
* Describe sample partial autocorrelation

Chapter 8: Modeling Cycles: MA, AR, and ARMA Models

* Describe the properties of the first-order moving average (MA(1)) process
* Describe the properties of a general finite-order process of order q (MA(q)) process
* Describe the properties of the first-order autoregressive (AR(1)) process, and define and explain the Yule-Walker equation.
* Describe the properties of a general pthorder autoregressive (AR(p)) process
* Define and describe the properties of the autoregressive moving average (ARMA) process.

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