Explanation:
Adding an appropriate lag is an appropriate solution for seasonally impacted data. Excluding variables can sometimes be used to solve multicollinearity. Transforming using a first-difference lag operator can be a cure for nonstationarity. ARMA models can be used for seasonal effects but are only considered when autocorrelations decay gradually.
(Book 2, Module 21.3, LO 21.n)
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Question 37
In an autoregressive (AR) time series model, seasonally impacted data may be corrected by:
A
excluding one or more of the lagged variables until the seasonal effects disappear.
B
transforming the time series using a first-difference lag operator.
C
adding a variable that reflects an appropriate lag of the time series.
D
applying an autoregressive moving average (ARMA) process to increase autocorrelations gradually.
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