dataset

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Axis dataset on sub-layer l1_a (layer l1).

Sub-layer

l1_a

Axis metadata

  • Default: 'fred_md'

  • Sweepable: True

  • Status: operational

Operational status summary

  • Operational: 5 option(s)

  • Future: 0 option(s)

Options

fred_md – operational

FRED-MD: 130+ monthly US macro series (1959-).

The McCracken & Ng (2016) Monthly Database for Macroeconomic Research. Curated set of ~130 macroeconomic and financial series with stable transformation codes, group tags, and a single vintage per month.

Default for monthly forecasting work; pairs with horizon_set: standard_md (h ∈ {1, 3, 6, 9, 12, 18, 24}) and frequency: monthly.

When to use

Monthly inflation, employment, industrial-production, and term-structure forecasting.

References

  • McCracken & Ng (2016) ‘FRED-MD: A Monthly Database for Macroeconomic Research’, Journal of Business & Economic Statistics 34(4). (doi:10.1080/07350015.2015.1086655)

Related options: custom_source_policy, frequency, horizon_set

Last reviewed 2026-05-04 by macroforecast author.

fred_qd – operational

FRED-QD: 250+ quarterly US macro series (1959-).

The McCracken & Ng (2020) Quarterly Database for Macroeconomic Research. Larger variable count than FRED-MD; quarterly cadence matches GDP / NIPA-style targets.

Default for quarterly forecasting; pairs with horizon_set: standard_qd (h ∈ {1, 2, 4, 8}) and frequency: quarterly.

When to use

GDP, consumption, investment, productivity nowcasting / forecasting.

References

  • McCracken & Ng (2020) ‘FRED-QD: A Quarterly Database for Macroeconomic Research’, Federal Reserve Bank of St. Louis Review.

Related options: custom_source_policy, frequency, horizon_set

Last reviewed 2026-05-04 by macroforecast author.

fred_sd – operational

FRED-SD: state-level US series with geographic axes.

State-level macro panel covering ~50 states + DC. Activates the L1.D geography axes (target_geography_scope / predictor_geography_scope) and the L7 us_state_choropleth figure type for spatial interpretation.

FRED-SD ships with mixed monthly + quarterly frequencies; the L2.A frequency-alignment rules (issue #202) handle the mixed case.

When to use

State-level employment / payroll / housing forecasting; geographic-importance studies.

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.’

Related options: custom_source_policy, frequency, horizon_set

Last reviewed 2026-05-04 by macroforecast author.

fred_md+fred_sd – operational

Joint FRED-MD + FRED-SD panel.

Concatenates the FRED-MD national series with FRED-SD state-level series on the date index. Useful when a study needs both national context (FRED-MD) and state-level granularity (FRED-SD) – e.g., a state-level employment forecast conditioned on national CPI.

When to use

Studies where state-level targets need national-aggregate predictors.

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.’

Related options: custom_source_policy, frequency, horizon_set

Last reviewed 2026-05-04 by macroforecast author.

fred_qd+fred_sd – operational

Joint FRED-QD + FRED-SD panel (quarterly + state-level mixed).

Concatenates FRED-QD with FRED-SD. Triggers the L2.A frequency-alignment rules because FRED-QD is quarterly while much of FRED-SD is monthly.

When to use

Quarterly state-level studies (rare; use only when the target is quarterly 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.’

Related options: custom_source_policy, frequency, horizon_set

Last reviewed 2026-05-04 by macroforecast author.