fred_sd_variable_group
Back to L1 | Browse all axes | Browse all options
Axis
fred_sd_variable_groupon sub-layerl1_d(layerl1).
Sub-layer
l1_d
Axis metadata
Default:
'all_sd_variables'Sweepable: False
Status: operational
Operational status summary
Operational: 12 option(s)
Future: 0 option(s)
Options
all_sd_variables – operational
All FRED-SD state-level variable categories.
FRED-SD variable category: Default. Includes every variable category in the FRED-SD groups manifest. Use as the broadest possible predictor block; subset via sd_variable_selection if specific filtering is needed.
Restricts the predictor block to series tagged with this category in the FRED-SD groups manifest. Combine with fred_sd_state_group to control geography and with sd_variable_selection to restrict further within this category.
When to use
Default; broadest predictor block.
References
macroforecast design Part 1, L1: ‘data definition is the recipe layer that pins source, target, geography, and horizon – everything downstream branches off these choices.’
McCracken & Ng (2020) ‘FRED-QD: A Quarterly Database for Macroeconomic Research’, Federal Reserve Bank of St. Louis Review.
Related options: labor_market_core, employment_sector, income
Last reviewed 2026-05-05 by macroforecast author.
labor_market_core – operational
Core labour-market series (employment, unemployment, hours).
FRED-SD variable category: Includes nonfarm employment, unemployment rate, labour-force participation, and average hours. Standard labour-market battery used in most state-level macroeconomic studies.
Restricts the predictor block to series tagged with this category in the FRED-SD groups manifest. Combine with fred_sd_state_group to control geography and with sd_variable_selection to restrict further within this category.
When to use
Labour-market focused studies; Sahm-rule recession analysis at state level.
References
macroforecast design Part 1, L1: ‘data definition is the recipe layer that pins source, target, geography, and horizon – everything downstream branches off these choices.’
McCracken & Ng (2020) ‘FRED-QD: A Quarterly Database for Macroeconomic Research’, Federal Reserve Bank of St. Louis Review.
Related options: all_sd_variables, employment_sector, income
Last reviewed 2026-05-05 by macroforecast author.
employment_sector – operational
Sectoral employment series (NAICS supersector breakdowns).
FRED-SD variable category: Sectoral employment counts (manufacturing, construction, services, government, etc.). Useful when industry mix explains target variation.
Restricts the predictor block to series tagged with this category in the FRED-SD groups manifest. Combine with fred_sd_state_group to control geography and with sd_variable_selection to restrict further within this category.
When to use
Industry-level employment studies; structural-transformation analysis.
References
macroforecast design Part 1, L1: ‘data definition is the recipe layer that pins source, target, geography, and horizon – everything downstream branches off these choices.’
McCracken & Ng (2020) ‘FRED-QD: A Quarterly Database for Macroeconomic Research’, Federal Reserve Bank of St. Louis Review.
Related options: all_sd_variables, labor_market_core, income
Last reviewed 2026-05-05 by macroforecast author.
gsp_output – operational
Gross state product / output series.
FRED-SD variable category: BEA gross state product (GSP), the state-level analogue of national GDP. Released quarterly with publication lag; main aggregate state-level output measure.
Restricts the predictor block to series tagged with this category in the FRED-SD groups manifest. Combine with fred_sd_state_group to control geography and with sd_variable_selection to restrict further within this category.
When to use
Aggregate output studies; state-level GDP forecasting.
References
macroforecast design Part 1, L1: ‘data definition is the recipe layer that pins source, target, geography, and horizon – everything downstream branches off these choices.’
McCracken & Ng (2020) ‘FRED-QD: A Quarterly Database for Macroeconomic Research’, Federal Reserve Bank of St. Louis Review.
Related options: all_sd_variables, labor_market_core, employment_sector
Last reviewed 2026-05-05 by macroforecast author.
housing – operational
State housing series (permits, prices, starts).
FRED-SD variable category: Building permits, housing starts, house-price indices. Leading indicator of state economic activity; central to any housing-cycle analysis.
Restricts the predictor block to series tagged with this category in the FRED-SD groups manifest. Combine with fred_sd_state_group to control geography and with sd_variable_selection to restrict further within this category.
When to use
Housing-cycle studies; foreclosure / mortgage-market analysis.
References
macroforecast design Part 1, L1: ‘data definition is the recipe layer that pins source, target, geography, and horizon – everything downstream branches off these choices.’
McCracken & Ng (2020) ‘FRED-QD: A Quarterly Database for Macroeconomic Research’, Federal Reserve Bank of St. Louis Review.
Related options: all_sd_variables, labor_market_core, employment_sector
Last reviewed 2026-05-05 by macroforecast author.
trade – operational
Trade / commerce series.
FRED-SD variable category: Retail sales, wholesale trade, port activity. State-level trade-flow indicators where available.
Restricts the predictor block to series tagged with this category in the FRED-SD groups manifest. Combine with fred_sd_state_group to control geography and with sd_variable_selection to restrict further within this category.
When to use
Trade-flow studies; port-region economic analysis.
References
macroforecast design Part 1, L1: ‘data definition is the recipe layer that pins source, target, geography, and horizon – everything downstream branches off these choices.’
McCracken & Ng (2020) ‘FRED-QD: A Quarterly Database for Macroeconomic Research’, Federal Reserve Bank of St. Louis Review.
Related options: all_sd_variables, labor_market_core, employment_sector
Last reviewed 2026-05-05 by macroforecast author.
income – operational
Personal income / earnings series.
FRED-SD variable category: Includes per-capita personal income, total state income, and components (wages, transfers, dividends). Slow-moving but persistent predictor of state economic activity.
Restricts the predictor block to series tagged with this category in the FRED-SD groups manifest. Combine with fred_sd_state_group to control geography and with sd_variable_selection to restrict further within this category.
When to use
Consumer / household income studies; transfer-payment analysis.
References
macroforecast design Part 1, L1: ‘data definition is the recipe layer that pins source, target, geography, and horizon – everything downstream branches off these choices.’
McCracken & Ng (2020) ‘FRED-QD: A Quarterly Database for Macroeconomic Research’, Federal Reserve Bank of St. Louis Review.
Related options: all_sd_variables, labor_market_core, employment_sector
Last reviewed 2026-05-05 by macroforecast author.
direct_analog_high_confidence – operational
Variables with direct national analog (high-confidence cross-frequency join).
FRED-SD variable category: FRED-SD variables that map directly onto a known FRED-MD / -QD national series at the same definition. The cleanest subset for cross-frequency studies that need national-state correspondence.
Restricts the predictor block to series tagged with this category in the FRED-SD groups manifest. Combine with fred_sd_state_group to control geography and with sd_variable_selection to restrict further within this category.
When to use
Cross-frequency studies needing direct national-state mapping.
References
macroforecast design Part 1, L1: ‘data definition is the recipe layer that pins source, target, geography, and horizon – everything downstream branches off these choices.’
McCracken & Ng (2020) ‘FRED-QD: A Quarterly Database for Macroeconomic Research’, Federal Reserve Bank of St. Louis Review.
Related options: all_sd_variables, labor_market_core, employment_sector
Last reviewed 2026-05-05 by macroforecast author.
provisional_analog_medium – operational
Variables with provisional national analog (medium-confidence join).
FRED-SD variable category: FRED-SD variables that approximately map onto a national series but with some definition mismatch (coverage gap, methodology change, etc.). Use with caution; the join is provisional.
Restricts the predictor block to series tagged with this category in the FRED-SD groups manifest. Combine with fred_sd_state_group to control geography and with sd_variable_selection to restrict further within this category.
When to use
Sensitivity analyses on the analog mapping.
References
macroforecast design Part 1, L1: ‘data definition is the recipe layer that pins source, target, geography, and horizon – everything downstream branches off these choices.’
McCracken & Ng (2020) ‘FRED-QD: A Quarterly Database for Macroeconomic Research’, Federal Reserve Bank of St. Louis Review.
Related options: all_sd_variables, labor_market_core, employment_sector
Last reviewed 2026-05-05 by macroforecast author.
semantic_review_outputs – operational
Outputs of the FRED-SD semantic review process.
FRED-SD variable category: Variables flagged through the FRED-SD semantic-review pipeline (audit-trail diagnostics produced by the FRED-SD construction process). Mostly used for diagnostic provenance, not as predictors.
Restricts the predictor block to series tagged with this category in the FRED-SD groups manifest. Combine with fred_sd_state_group to control geography and with sd_variable_selection to restrict further within this category.
When to use
Audit-trail diagnostics for the FRED-SD construction process.
References
macroforecast design Part 1, L1: ‘data definition is the recipe layer that pins source, target, geography, and horizon – everything downstream branches off these choices.’
McCracken & Ng (2020) ‘FRED-QD: A Quarterly Database for Macroeconomic Research’, Federal Reserve Bank of St. Louis Review.
Related options: all_sd_variables, labor_market_core, employment_sector
Last reviewed 2026-05-05 by macroforecast author.
no_reliable_analog – operational
Variables without a reliable national analog.
FRED-SD variable category: FRED-SD-only series that have no clean correspondence to a FRED-MD / -QD national variable. Useful for state-only studies that exclude national benchmarks.
Restricts the predictor block to series tagged with this category in the FRED-SD groups manifest. Combine with fred_sd_state_group to control geography and with sd_variable_selection to restrict further within this category.
When to use
State-only studies; spatial-econometric panels that ignore national aggregates.
References
macroforecast design Part 1, L1: ‘data definition is the recipe layer that pins source, target, geography, and horizon – everything downstream branches off these choices.’
McCracken & Ng (2020) ‘FRED-QD: A Quarterly Database for Macroeconomic Research’, Federal Reserve Bank of St. Louis Review.
Related options: all_sd_variables, labor_market_core, employment_sector
Last reviewed 2026-05-05 by macroforecast author.
custom_sd_variable_group – operational
User-supplied variable list (leaf_config.custom_sd_variables).
FRED-SD variable category: Bespoke variable selections – e.g. ‘manufacturing + trade only’ or ‘a specific BLS series list’. Reads the explicit variable list from leaf_config.custom_sd_variables.
Restricts the predictor block to series tagged with this category in the FRED-SD groups manifest. Combine with fred_sd_state_group to control geography and with sd_variable_selection to restrict further within this category.
When to use
Bespoke variable selections not captured by built-in groupings.
References
macroforecast design Part 1, L1: ‘data definition is the recipe layer that pins source, target, geography, and horizon – everything downstream branches off these choices.’
McCracken & Ng (2020) ‘FRED-QD: A Quarterly Database for Macroeconomic Research’, Federal Reserve Bank of St. Louis Review.
Related options: all_sd_variables, labor_market_core, employment_sector
Last reviewed 2026-05-05 by macroforecast author.