Final answer:
Seasonally adjusted data is most useful for analyzing trends over time that might be affected by recurring seasonal events, providing a clearer view of underlying economic trends.
For instance, it helps economists understand employment or GDP trends without the noise of seasonal patterns. When dealing with immediate stock market data or public opinion surveys without seasonal influence, raw data might be more appropriate.
Step-by-step explanation:
It is appropriate to use seasonally adjusted data when you are using quarterly data to describe or predict trends that occur over the course of a year. This adjustment helps to remove the impacts of recurring seasonal events from economic time series, allowing for a clearer comparison of economic performance across periods.
For instance, an economist may want to use seasonally adjusted figures to assess the trends in unemployment rates, which naturally fluctuate throughout the year due to seasonal hiring.
An economist deriving a model to predict stock market outcomes will often work with raw data of daily fluctuations. However, when considering larger trends and making long-term predictions, seasonally adjusted data might give a clearer picture of underlying trends free from regular seasonal noise.
Recognition lags are a challenge in timely economic analysis. Economic data are often available only after delays and are subject to revisions, which can misrepresent the initial estimates of economic indicators like real GDP. In the context of evaluating performance right in the middle of events, such as an ongoing financial quarter, seasonally adjusted data can help identify the actual trends as opposed to seasonal effects.
Similarly, when representing data graphically, the use of figures such as five-year averages can help smooth out year-to-year changes and reduce perceived variability, aiding in identifying longer-term trends in economic indicators like unemployment rates.
In the context of public opinion surveys on current affairs, seasonality might not be as relevant unless the topic surveyed has seasonal variations itself. For instance, holiday-related consumer sentiments might benefit from seasonally adjusted comparisons.