Source: U.S. Bureau of Labor Statistics  

Release: State Employment and Unemployment  

Units:  Thousands of Persons, Seasonally Adjusted

Frequency:  Monthly

The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodels' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodels' X-13ARIMA-SEATS package can be found here. More information on X-13ARIMA-SEATS can be found here.

Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here.

Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator.

Source: U.S. Bureau of Labor Statistics  

Release: State Employment and Unemployment  

Units:  Thousands of Persons, Seasonally Adjusted

Frequency:  Monthly

The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodels' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodels' X-13ARIMA-SEATS package can be found here. More information on X-13ARIMA-SEATS can be found here.

Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here.

Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator.

Source: Federal Reserve Bank of Dallas  

Release: Texas Employment Data  

Units:  Thousands of Persons, Seasonally Adjusted

Frequency:  Monthly

The Dallas Fed has improved the quality of the payroll employment estimates for Metropolitan Areas of Texas using early benchmarking and two-step seasonal adjustment. More information regarding the early benchmarking technique can be found at http://www.dallasfed.org/research/basics/benchmark.cfm. More information pertaining to two-step seasonal adjustment can be found at http://www.dallasfed.org/research/basics/twostep.cfm.

Source: U.S. Bureau of Labor Statistics  

Source: Federal Reserve Bank of St. Louis  

Release: State and Metro Area Employment, Hours, and Earnings  

Units:  Thousands of Persons, Seasonally Adjusted

Frequency:  Monthly

The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodels' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodels' X-13ARIMA-SEATS package can be found here. More information on X-13ARIMA-SEATS can be found here.

Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here.

Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator.