Weak stationarity is less practical than strict (strong) stationarity concept. That's why the latter is used more often than the former

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The statement to evaluate is: Weak stationarity is less practical than strict (strong) stationarity, which is why the latter is used more often than the former.
First, let’s recall the definitions: weak (or second-order) stationarity requires constant mean, constant variance, and autocovariance depending only on the lag, not on time. Strict (or strong) stationarity requires that the entire joint distribution of any collection of time points is invariant to time shifts. Because strong stationarity imposes a much stronger condition, it is generally much harder to verify in practice.
On the other hand, weak stationarity is typically easier to check and sufficient for many common time series techniques, such as ARMA modeling, where the analysis relies on second-order properties (mean, variance, autocovariances) rather than the full distribution. This practical flexibility is why many applied approaches rely on weak stationarity rather than strict stationarity.
Option 1: True. This asserts that weak stationarity is less practical and that strict stationarity is used more often for practicality. In reality, the reverse is commonly observed: weak stationarity is the more practical, widely used assumption because it suffices for most modeling and inference tasks, while strict stationarity is rarely required or tested directly in applied work. Therefore, the claim in option 1 mischaracterizes typical practice.
Option 2: False. This asserts that the statement is not correct. Given the practical considerations described, this option aligns with how time series analysts approach stationarity: weak stationarity is preferred for practical modeling because it is easier to satisfy and sufficient for many methods, whereas strict stationarity is indeed harder to verify and not routinely assumed in everyday analyses. The distinction between practicality and necessity supports the falsehood of the original claim.
To evaluate the two choices, the core misconception in the first option is conflating the ease of verification and the sufficiency for modeling with an overall assertion about which is used more often. The second option corrects that by recognizing that the common practice leans toward weak stationarity due to its sufficiency for many methods and its tractability, not toward the stronger, less verifiable notion of strict stationarity.
In summary, the nuance lies in practicality versus theoretical strength: strict stationarity is not the commonly used default in applied settings, because weak stationarity provides the needed structure for many analyses with far fewer verification hurdles.

AS.440.617.80.SP25 Quiz 4. Time Series regression

Weak stationarity is less practical than strict (strong) stationarity concept. That's why the latter is used more often than the former

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