Explanation:
Random walks are data containing a unit root, which makes them difficult to analyze in the traditional AR, MA, ARMA, etc., time series modeling sense. One of the characteristics of data containing a unit root is that it has an infinite variance, which means it is not covariance stationary; hence, it cannot be modeled in the traditional sense.
(Book 2, Module 23.2, LO 23.c)
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Question 52
A quantitative analyst at Shipment Specialists knows that random walks contain a unit root, which makes things tricky when it comes to modeling. Which of the following characteristics of data with unit roots cause problems when modeling time series?
A
Data containing a unit root is covariance stationary.
B
Data containing a unit root has infinite variance.
C
Data containing a unit root generates a long-run mean-reverting level.
D
Data containing a unit root has finite variance.
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