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  • Percent, Monthly, Seasonally Adjusted

  • Thousands of Persons, Monthly, Seasonally Adjusted

    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. 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/SMU56000001021210001). 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."

  • Thousands of Persons, Monthly, Seasonally Adjusted

  • Persons, Monthly, Not Seasonally Adjusted

    The U.S. Census Bureau provides annual estimates of income and poverty statistics for all school districts, counties, and states through the Small Area Income and Poverty Estimates (https://www.census.gov/programs-surveys/saipe/about.html) (SAIPE) program. The bureau's main objective with this program is to provide estimates of income and poverty for the administration of federal programs and the allocation of federal funds to local jurisdictions. In addition to these federal programs, state and local programs use the income and poverty estimates for distributing funds and managing programs. SNAP benefits are one of the data sources used in producing SAIPE program estimates. The Supplemental Nutrition Assistance Program (SNAP) is the name for what was formerly known as the federal Food Stamp Program, as of October 1, 2008. The SNAP benefits data represent the number of participants in the Supplemental Nutrition Assistance Program for each county, state, and the District of Columbia from 1981 to the latest available year. See more details about SAIPE Model Input Data (https://www.census.gov/data/datasets/time-series/demo/saipe/model-tables.html).

  • Percent, Monthly, Seasonally Adjusted

    A state's labor-force participation rate is the number of all employed and unemployed workers divided against the state's civilian population. Differences between monthly seasonally-adjusted and not-seasonally-adjusted labor force participation rates are determined by the seasonal components of the LAUS labor force levels. The not seasonally adjusted version of this time series is LBSNSA56

  • Thousands of Persons, Monthly, Seasonally Adjusted

    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. 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/SMU56000001021311201). 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."

  • Persons, Monthly, Seasonally Adjusted

  • Percent, Monthly, Seasonally Adjusted

    The leading index for each state predicts the six-month growth rate of the state's coincident index. In addition to the coincident index, the models include other variables that lead the economy: state-level housing permits (1 to 4 units), state initial unemployment insurance claims, delivery times from the Institute for Supply Management (ISM) manufacturing survey, and the interest rate spread between the 10-year Treasury bond and the 3-month Treasury bill.

  • Millions of Dollars, Monthly, Not Seasonally Adjusted

    Imports of manufactured and non-manufactured commodities based on destination. All NAICS-Based Product Codes.

  • Index 2007=100, Monthly, Seasonally Adjusted

    The Coincident Economic Activity Index includes four indicators: nonfarm payroll employment, the unemployment rate, average hours worked in manufacturing and wages and salaries. The trend for each state's index is set to match the trend for gross state product. 

  • Thousands of Persons, Monthly, Seasonally Adjusted

    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. 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/SMU56000006562200001). 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."

  • Level, Monthly, Not Seasonally Adjusted

    The count of listings which have had their price increased in a given market during the month.

  • Level, Monthly, Not Seasonally Adjusted

    The median number of days property listings spend on the market in a given geography during the specified month (calculated from list date to closing, pending, or off-market date depending on data availability).

  • Level, Monthly, Not Seasonally Adjusted

    The count of new listings added to the market in a given geography during the month.

  • Level, Monthly, Not Seasonally Adjusted

    The count of pending listings in a given market during the specified month, if a pending definition is available for that geography.

  • U.S. Dollars, Monthly, Not Seasonally Adjusted

    The median listing price in a given market during the specified month.

  • U.S. Dollars, Monthly, Not Seasonally Adjusted

    The average listing price in a given market during the specified month.

  • Level, Monthly, Not Seasonally Adjusted

    The count of active single-family and condo/townhome listings for a given market during the specified month (excludes pending listings).

  • Millions of Dollars, Monthly, Not Seasonally Adjusted

    Exports of manufactured and non-manufactured commodities based on origin of movement. All NAICS-Based Product Codes.

  • Thousands of Persons, Monthly, Seasonally Adjusted

  • Thousands of Persons, Monthly, Seasonally Adjusted

  • Thousands of Persons, Monthly, Seasonally Adjusted

  • Thousands of Persons, Monthly, Seasonally Adjusted

  • Thousands of Persons, Monthly, Seasonally Adjusted

  • Millions of Dollars, Monthly, Not Seasonally Adjusted

    Exports of manufactured commodities based on origin of movement. Includes NAICS-Based Product Codes 31-33.

  • Millions of Dollars, Monthly, Not Seasonally Adjusted

    Exports of non-manufactured commodities based on origin of movement. All NAICS-Based Product Codes excluding 31-33.

  • Millions of Dollars, Monthly, Not Seasonally Adjusted

    Imports of manufactured commodities based on destination. Includes NAICS-Based Product Codes 31-33.

  • Millions of Dollars, Monthly, Not Seasonally Adjusted

    Imports of non-manufactured commodities based on destination. NAICS-Based Product Codes excluding 31-33.

  • U.S. Dollars, Monthly, Not Seasonally Adjusted

    The median listing price per square foot in a given market during the specified month.

  • Units, Monthly, Seasonally Adjusted

    This series represents the total number of building permits for all structure types. Structure types include 1-unit, 2-unit, 3-unit, 4-unit, and 5-unit or more. 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. 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/WYBPPRIV). 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.

  • Thousands of Persons, Monthly, Seasonally Adjusted

  • Thousands of Persons, Monthly, Seasonally Adjusted

  • Thousands of Persons, Monthly, Seasonally Adjusted

  • Thousands of Persons, Monthly, Seasonally Adjusted

    This series is seasonally adjusted by the U.S. Bureau of Labor Statistics.

  • Persons, Monthly, Seasonally Adjusted

    These data come from the Current Population Survey (CPS), also known as the household survey. Employed persons are all persons who, during the reference week (the week including the 12th day of the month), (a) did any work as paid employees, worked in their own business or profession or on their own farm, or worked 15 hours or more as unpaid workers in an enterprise operated by a member of their family, or (b) were not working but who had jobs from which they were temporarily absent because of vacation, illness, bad weather, childcare problems, maternity or paternity leave, labor-management dispute, job training, or other family or personal reasons, whether or not they were paid for the time off or were seeking other jobs. Each employed person is counted only once, even if he or she holds more than one job. For more details, see the release's frequently asked questions (https://www.bls.gov/lau/laufaq.htm).

  • Persons, Monthly, Seasonally Adjusted

    These data come from the Current Population Survey (CPS), also known as the household survey. Unemployed persons are all persons who had no employment during the reference week, were available for work, except for temporary illness, and had made specific efforts to find employment some time during the 4 week-period ending with the reference week. Persons who were waiting to be recalled to a job from which they had been laid off need not have been looking for work to be classified as unemployed. For more details, see the release's frequently asked questions (https://www.bls.gov/lau/laufaq.htm).

  • Thousands of Persons, Monthly, Seasonally Adjusted

  • Units, Monthly, Seasonally Adjusted

    This series represents the total number of building permits for 1-unit structure types. The 1-unit structure type is a single-family home or house. A single-family home or house types include fully detached, semidetached (semidetached, side-by-side), row houses, and townhouses. For attached units, each must be separated from the adjacent unit by a ground-to-roof wall in order to be classified as a single-family structure. These units also must not share heating/air-conditioning systems or utilities. Units built on top of one another and those built side-by-side that do not have a ground-to-roof wall and/or have common facilities (i.e., attic, basement, heating plant, plumbing, etc.) are not included under single-family structures. 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. 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/WYBP1FH). 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.

  • Index Jan 1988=100, Monthly, Not Seasonally Adjusted

    These indexes calculate the inflation-adjusted value of the U.S. dollar against the currencies of countries to which the state exports. The real exchange rates are aggregated across countries for each state using the annual average export share to the country. For the most recent year where export share data is not available, the prior year' number is used instead. The indexes should allow analysts to more precisely identify the exchange rate movements that most affect demand for a state's exports. For more information visit http://www.dallasfed.org/research/econdata/rtwvd.cfm.

  • Thousands of Persons, Monthly, Seasonally Adjusted

    This series is seasonally adjusted by the U.S. Bureau of Labor Statistics.

  • Thousands of Persons, Monthly, Seasonally Adjusted

    This series is seasonally adjusted by the U.S. Bureau of Labor Statistics.

  • 3-month Average Change, Thousands of Persons, Monthly, Seasonally Adjusted

  • Thousands of Persons, Monthly, Seasonally Adjusted

    This series is seasonally adjusted by the U.S. Bureau of Labor Statistics.

  • Thousands of Persons, Monthly, Seasonally Adjusted

    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. 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/SMU56000004340008901). 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."

  • Dollars per Week, Monthly, Seasonally Adjusted

    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. 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/SMU56000008000000011). 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."

  • Dollars per Week, Monthly, Seasonally Adjusted

    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. 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/SMU56000002000000011). 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."

  • Thousands of Persons, Monthly, Seasonally Adjusted

    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. 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/SMU56000004348400001). 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."

  • Thousands of Persons, Monthly, Seasonally Adjusted

    This series is seasonally adjusted by the U.S. Bureau of Labor Statistics.

  • Thousands of Persons, Monthly, Seasonally Adjusted

    This series is seasonally adjusted by the U.S. Bureau of Labor Statistics.

  • Dollars per Week, Monthly, Seasonally Adjusted

    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. 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/SMU56000000500000011). 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."


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