This statistic displays the seasonally adjusted number of job losers and persons who completed temporary jobs in the United States on a monthly basis. Seasonal adjustment is a statistical method for removing the seasonal component of a time series that is used when analyzing non-seasonal trends. In January 2019, the number of job losers and persons who completed temporary jobs stood at about 3.08 million. The monthly unemployment rate can be found here.
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Monthly number of job losers in the U.S. from January 2018 to January 2019 (in millions, adjusted)
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This statistic displays the seasonally adjusted number of job losers and persons who completed temporary jobs in the United States on a monthly basis. Seasonal adjustment is a statistical method for removing the seasonal component of a time series that is used when analyzing non-seasonal trends. In January 2019, the number of job losers and persons who completed temporary jobs stood at about 3.08 million. The monthly unemployment rate can be found here.
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This statistic displays the seasonally adjusted number of job losers and persons who completed temporary jobs in the United States on a monthly basis. Seasonal adjustment is a statistical method for removing the seasonal component of a time series that is used when analyzing non-seasonal trends. In January 2019, the number of job losers and persons who completed temporary jobs stood at about 3.08 million. The monthly unemployment rate can be found here.
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- Release date
- February 2019
- Region
- United States
- Survey time period
- January 2018 to January 2019
- Age group
- 16 years and older
- Supplementary notes
- Seasonal adjustment is a statistical method for removing the seasonal component of a time series that is used when analyzing non-seasonal trends. It is normal to report un-adjusted data for current unemployment rates, as these reflect the actual current situation. Seasonally adjusted data may be used for the longer term comparison. Unlike the trend and cyclical components, seasonal components, theoretically, happen with similar magnitude during the same time period each year. The seasonal component of a series are often considered to be uninteresting in their own right and to cause the interpretation of a series to be ambiguous. By removing the seasonal component, it is easier to focus on other components.
More information
- Region
- United States
- Survey time period
- January 2018 to January 2019
- Age group
- 16 years and older
- Supplementary notes
- Seasonal adjustment is a statistical method for removing the seasonal component of a time series that is used when analyzing non-seasonal trends. It is normal to report un-adjusted data for current unemployment rates, as these reflect the actual current situation. Seasonally adjusted data may be used for the longer term comparison. Unlike the trend and cyclical components, seasonal components, theoretically, happen with similar magnitude during the same time period each year. The seasonal component of a series are often considered to be uninteresting in their own right and to cause the interpretation of a series to be ambiguous. By removing the seasonal component, it is easier to focus on other components.
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