NOTES
| Title | Release Dates | |
|
|
||
| All Employees: Information in La Crosse, WI-MN (MSA) | 2012-01-24 | 2015-03-26 |
| All Employees: Information in La Crosse-Onalaska, WI-MN (MSA) | 2015-03-27 | 2024-11-19 |
| Source | ||
|
|
||
| Federal Reserve Bank of St. Louis | 2012-01-24 | 2017-05-18 |
| U.S. Bureau of Labor Statistics | 2017-05-19 | 2024-11-19 |
| Release | ||
|
|
||
| Regional and State Employment and Unemployment (Seasonally Adjusted) | 2012-01-24 | 2017-03-12 |
| State Employment and Unemployment (Seasonally Adjusted) | 2017-03-13 | 2017-05-18 |
| State Employment and Unemployment | 2017-05-19 | 2024-11-19 |
| Units | ||
|
|
||
| Thousands of Persons | 2012-01-24 | 2024-11-19 |
| Frequency | ||
|
|
||
| Monthly | 2012-01-24 | 2024-11-19 |
| Seasonal Adjustment | ||
|
|
||
| Seasonally Adjusted | 2012-01-24 | 2024-11-19 |
| Notes | ||
|
|
||
|
The data services of the Federal Reserve Bank of St. Louis include series that are seasonally adjusted. To make these adjustments, we use the X-12 Procedure of SAS to remove the seasonal component of the series so that non-seasonal trends can be analyzed. This procedure is based on the U.S. Bureau of the Census X-12-ARIMA Seasonal Adjustment Program. More information on this program can be found at http://www.census.gov/srd/www/x12a/. The seasonal moving average function used is that of the Census Bureau’s X-11-ARIMA program. This includes a 3x3 moving average for the initial seasonal factors and a 3x5 moving average to calculate the final seasonal factors. The D11 function is also used to output the entire seasonally adjusted series that is displayed. For specific information on the SAS X-12 procedure, please visit their website: http://support.sas.com/documentation/cdl/en/etsug/60372/HTML/default/viewer.htm#etsug_x12_sect001.htm. |
2012-01-24 | 2017-05-18 |
|
The data services of the Federal Reserve Bank of St. Louis include series that are seasonally adjusted. To make these adjustments, we use the X-12 Procedure of SAS to remove the seasonal component of the series so that non-seasonal trends can be analyzed. This procedure is based on the U.S. Bureau of the Census X-12-ARIMA Seasonal Adjustment Program. More information on this program can be found at https://www.census.gov/srd/www/x13as/. The seasonal moving average function used is that of the Census Bureau’s X-11-ARIMA program. This includes a 3x3 moving average for the initial seasonal factors and a 3x5 moving average to calculate the final seasonal factors. The D11 function is also used to output the entire seasonally adjusted series that is displayed. For specific information on the SAS X-12 procedure, please visit their website: http://support.sas.com/documentation/cdl/en/etsug/60372/HTML/default/viewer.htm#etsug_x12_sect001.htm. |
2017-05-19 | 2019-03-05 |
|
The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' 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 'statsmodel' X-13ARIMA-SEATS package can be found at https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html. More information on X-13ARIMA-SEATS can be found at https://www.census.gov/srd/www/x13as/. 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. |
2019-03-06 | 2019-08-26 |
|
The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' 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 'statsmodel' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/srd/www/x13as/). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. In some cases, the NSA data will be updated but the SA data will not be updated. The reason is usually that the data series has not accumulated enough new seasonal factors to trigger an adjustment.The NSA series can be located here (https://fred.stlouisfed.org/series/LACR155INFON) The FRED team is currently working on a new procedure to replace SA data that has not yet be updated with NSA data that has been updated. 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. |
2019-08-27 | 2019-12-19 |
|
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 (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/data/software/x13as.html). 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 (https://fred.stlouisfed.org/series/LACR155INFON). 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. |
2019-12-20 | 2024-11-19 |
RELATED DATA AND CONTENT
Data Suggestions Based On Your Search
Content Suggestions
Other Formats
Related Categories
La Crosse-Onalaska, WI-MN
MSAs
Wisconsin
La Crosse-Onalaska, WI-MN
MSAs
Minnesota
States
U.S. Regional Data