1Y |
5Y |
10Y |
Max
NOTES
| Title | Release Dates | |
|
|
||
| Total Quarterly Wages in Washington-Arlington-Alexandria, DC-VA-MD-WV (MSA) | 2015-06-17 | 2019-06-05 |
| Source | ||
|
|
||
| Federal Reserve Bank of St. Louis | 2015-06-17 | 2019-03-05 |
| U.S. Bureau of Labor Statistics | 2015-06-17 | 2019-03-05 |
| Federal Reserve Bank of St. Louis | 2019-03-06 | 2019-06-05 |
| U.S. Bureau of Labor Statistics | 2019-03-06 | 2019-06-05 |
| Release | ||
|
|
||
| Quarterly Census of Employment and Wages | 2015-06-17 | 2019-06-05 |
| Units | ||
|
|
||
| Dollars | 2015-06-17 | 2019-06-05 |
| Frequency | ||
|
|
||
| Quarterly | 2015-06-17 | 2019-06-05 |
| Seasonal Adjustment | ||
|
|
||
| Seasonally Adjusted | 2015-06-17 | 2019-06-05 |
| Notes | ||
|
|
||
|
Total wages are the wages paid by Unemployment Insurance covered employers during the calendar quarter, regardless of when the services were performed. The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'x12' package from R with default parameter settings. The package uses the U.S. Bureau of the Census X-12-ARIMA Seasonal Adjustment Program. More information on the 'x12' package can be found at http://cran.r-project.org/web/packages/x12/x12.pdf. More information on X-12-ARIMA can be found at https://www.census.gov/srd/www/x13as/. |
2015-06-17 | 2019-03-05 |
|
Total wages are the wages paid by Unemployment Insurance covered employers during the calendar quarter, regardless of when the services were performed. 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 | 2019-06-05 |
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