horizon_set
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Axis
horizon_seton sub-layerl1_f(layerl1).
Sub-layer
l1_f
Axis metadata
Default:
'derived'Sweepable: False
Status: operational
Operational status summary
Operational: 5 option(s)
Future: 0 option(s)
Options
standard_md – operational
Standard FRED-MD horizons: {1, 3, 6, 9, 12, 18, 24} months.
The canonical multi-horizon set used in the McCracken-Ng / Stock-Watson tradition for monthly forecasting. Models are fit per-horizon (when forecast_strategy = direct) and metrics report per-(model, horizon) rows.
When to use
Default for monthly studies. Comparable to published monthly benchmarks.
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 (2016) ‘FRED-MD: A Monthly Database for Macroeconomic Research’, Journal of Business & Economic Statistics 34(4). (doi:10.1080/07350015.2015.1086655)
Related options: standard_qd, single, custom_list, range_up_to_h
Last reviewed 2026-05-04 by macroforecast author.
standard_qd – operational
Standard FRED-QD horizons: {1, 2, 4, 8} quarters.
Quarterly counterpart of standard_md.
Configures the horizon_set axis on l1_f (layer l1); the standard_qd value is materialised in the recipe’s fixed_axes block under that sub-layer.
When to use
Default for quarterly (FRED-QD) 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.’
McCracken & Ng (2020) ‘FRED-QD: A Quarterly Database for Macroeconomic Research’, Federal Reserve Bank of St. Louis Review.
Related options: standard_md, single, custom_list
Last reviewed 2026-05-04 by macroforecast author.
single – operational
A single horizon (defaults to h=1).
Forecasts only one horizon per cell. Sets leaf_config.target_horizons = [N] to override the default of 1. Faster than multi-horizon studies and clearer metrics tables when the study’s question is single-horizon.
When to use
One-shot studies (h=1 nowcasting, h=12 long-horizon ablation).
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: standard_md, standard_qd, custom_list
Last reviewed 2026-05-04 by macroforecast author.
custom_list – operational
User-supplied horizon list (any non-empty integer set).
Requires leaf_config.target_horizons: [int...]. Useful for non-standard horizon comparisons (e.g., {1, 2, 3, 6, 12} or {6, 12, 24, 36}).
When to use
Replication of papers with non-standard horizon sets; ablation 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: standard_md, standard_qd, range_up_to_h
Last reviewed 2026-05-04 by macroforecast author.
range_up_to_h – operational
Every horizon from 1 to leaf_config.max_horizon (inclusive).
Equivalent to custom_list with [1, 2, ..., max_horizon]. Useful for direct-h forecasting where the study wants dense horizon coverage (e.g., 1-12 months).
When to use
Dense horizon studies with direct-h 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.’
Related options: custom_list, standard_md
Last reviewed 2026-05-04 by macroforecast author.