Explanation:
Statement I is incorrect because the ARMA process does not exclude lagged random shocks and lagged observations; instead, it relies heavily on them.
Statement II is correct. The ARMA model combines the Autoregressive (AR) model, which relies on past observations of the time series, with the Moving Average (MA) model, which relies on past unobservable random shocks (errors).
Statement III is correct. ARMA processes typically exhibit gradually-decaying autocorrelations due to the AR component, which passes the effects of past shocks down the line rather than cutting them off abruptly like a pure MA process.
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Q.22 Which of the following statements is/are accurate? The autoregressive moving average (ARMA) process forms an important part of time series analysis since it:
I. Captures a very robust picture of the variable being estimated thanks to the exclusion of lagged random shocks and lagged observations
II. Combines the lagged unobservable random shock characteristic of the MA process with the observed lagged time series characteristic of the AR process
III. Involves gradually-decaying autocorrelations
A
I & II only
B
II & III only
C
I & III only
D
All of the above