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Data Seasonality with FRED
Economic activity increases and decreases over time for a variety of reasons―some unexpected and others predictable. Economic data reflect those changing conditions, and it’s important to understand whether the upswings and downswings are long-lasting or short-lived.
For example, some types of economic activity slowly become obsolete―such as renting DVDs to watch movies at home―and the data reflect that trend by showing a long-term decline in output or employment. At the same time, overall economic activity, measured through gross domestic product, periodically experiences medium-length periods of expansion and contraction known as business cycles. And, specific sectors of economic activity, such as farming or retail sales, record short-lived highs and lows that follow a yearly calendar schedule. These are called seasonal patterns. Data with these patterns are said to experience seasonality.
Seasonality in the data can mislead or confuse an otherwise straightforward interpretation of monthly or quarterly rates of change. In other words, data users can misinterpret short-term seasonal highs or lows as the start of longer-lasting cyclical booms or crashes. Researchers can use a mathematical process called seasonal adjustment to remove the seasonal patterns in the data.
Data in FRED are always labeled as either “not seasonally adjusted” or “seasonally adjusted.” Those labels reflect whether the source reports the raw data or the data after they’re adjusted to remove the variability from seasonal patterns.
This article describes how to identify seasonal patterns in the data and how to best tell the story behind the numbers, whether they are seasonally adjusted or not.
For example, some types of economic activity slowly become obsolete―such as renting DVDs to watch movies at home―and the data reflect that trend by showing a long-term decline in output or employment. At the same time, overall economic activity, measured through gross domestic product, periodically experiences medium-length periods of expansion and contraction known as business cycles. And, specific sectors of economic activity, such as farming or retail sales, record short-lived highs and lows that follow a yearly calendar schedule. These are called seasonal patterns. Data with these patterns are said to experience seasonality.
Seasonality in the data can mislead or confuse an otherwise straightforward interpretation of monthly or quarterly rates of change. In other words, data users can misinterpret short-term seasonal highs or lows as the start of longer-lasting cyclical booms or crashes. Researchers can use a mathematical process called seasonal adjustment to remove the seasonal patterns in the data.
Data in FRED are always labeled as either “not seasonally adjusted” or “seasonally adjusted.” Those labels reflect whether the source reports the raw data or the data after they’re adjusted to remove the variability from seasonal patterns.
This article describes how to identify seasonal patterns in the data and how to best tell the story behind the numbers, whether they are seasonally adjusted or not.
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