The reason we use moving averages in the first place is that a lot of time-series data is *noisy; *due to errors in data collection or random fluctuations in the real-world phenomenon, the data jumps up and down randomly, obscuring any long-term trends. A good example of this is GDP...

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The reason we use moving averages in the first place is that a lot of time-series data is *noisy; *due to errors in data collection or random fluctuations in the real-world phenomenon, the data jumps up and down randomly, obscuring any long-term trends.

A good example of this is GDP (linked below). If you measure GDP growth over very short periods, it goes all over the place; but if you use a moving average over a longer period like a year, a much smoother pattern forms, clearly showing periods of prosperity and recession; and then if you use a moving average over even longer periods like decades, you can see the long-run trend in productivity growth.

The answer is therefore *f*** alse**; if you use a smaller number of periods for your moving average, it will pick up more of the random short-run fluctuations and therefore less of the long-run trend.

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