dfm_diagnostics
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Axis
dfm_diagnosticson sub-layerL3_5_B_factor_block_inspection(layerl3_5).
Sub-layer
L3_5_B_factor_block_inspection
Axis metadata
Default:
'multi'Sweepable: False
Status: operational
Operational status summary
Operational: 4 option(s)
Future: 0 option(s)
Options
factor_var_stability – operational
Plot of DFM factor-VAR coefficient stability over time.
L3.5.B DFM diagnostic factor_var_stability.
This option configures the dfm_diagnostics axis on the L3_5_B_factor_block_inspection sub-layer of L3.5; output is emitted under manifest.diagnostics/l3_5/L3_5_B_factor_block_inspection/ alongside the other selected views.
When to use
Detecting non-stationarity in the factor dynamics; rolling-window estimates flag breaks.
References
macroforecast design Part 4: ‘diagnostic layers default-off; non-blocking; produce JSON + matplotlib views attached to manifest.diagnostics/.’
Mariano & Murasawa (2003) ‘A new coincident index of business cycles based on monthly and quarterly series’, JAE 18(4): 427-443.
Related options: idiosyncratic_acf, multi, none
Last reviewed 2026-05-05 by macroforecast author.
idiosyncratic_acf – operational
Autocorrelation of DFM idiosyncratic residuals.
L3.5.B DFM diagnostic idiosyncratic_acf.
This option configures the dfm_diagnostics axis on the L3_5_B_factor_block_inspection sub-layer of L3.5; output is emitted under manifest.diagnostics/l3_5/L3_5_B_factor_block_inspection/ alongside the other selected views.
When to use
Validating the idiosyncratic-AR(1) assumption; large residual ACF at lags > 1 indicates misspecification.
References
macroforecast design Part 4: ‘diagnostic layers default-off; non-blocking; produce JSON + matplotlib views attached to manifest.diagnostics/.’
Mariano & Murasawa (2003) ‘A new coincident index of business cycles based on monthly and quarterly series’, JAE 18(4): 427-443.
Related options: factor_var_stability, multi, none
Last reviewed 2026-05-05 by macroforecast author.
multi – operational
Render both DFM diagnostics together.
L3.5.B DFM diagnostic multi.
This option configures the dfm_diagnostics axis on the L3_5_B_factor_block_inspection sub-layer of L3.5; output is emitted under manifest.diagnostics/l3_5/L3_5_B_factor_block_inspection/ alongside the other selected views.
When to use
Comprehensive DFM validation; recommended after any DFM fit.
References
macroforecast design Part 4: ‘diagnostic layers default-off; non-blocking; produce JSON + matplotlib views attached to manifest.diagnostics/.’
Mariano & Murasawa (2003) ‘A new coincident index of business cycles based on monthly and quarterly series’, JAE 18(4): 427-443.
Related options: factor_var_stability, idiosyncratic_acf, none
Last reviewed 2026-05-05 by macroforecast author.
none – operational
Skip DFM-specific diagnostics.
L3.5.B DFM diagnostic none.
This option configures the dfm_diagnostics axis on the L3_5_B_factor_block_inspection sub-layer of L3.5; output is emitted under manifest.diagnostics/l3_5/L3_5_B_factor_block_inspection/ alongside the other selected views.
When to use
Pipelines without DFM blocks (PCA-only or no-factor pipelines).
References
macroforecast design Part 4: ‘diagnostic layers default-off; non-blocking; produce JSON + matplotlib views attached to manifest.diagnostics/.’
Mariano & Murasawa (2003) ‘A new coincident index of business cycles based on monthly and quarterly series’, JAE 18(4): 427-443.
Related options: factor_var_stability, idiosyncratic_acf, multi
Last reviewed 2026-05-05 by macroforecast author.