factor_view

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Axis factor_view on sub-layer L3_5_B_factor_block_inspection (layer l3_5).

Sub-layer

L3_5_B_factor_block_inspection

Axis metadata

  • Default: 'multi'

  • Sweepable: False

  • Status: operational

Operational status summary

  • Operational: 5 option(s)

  • Future: 0 option(s)

Options

cumulative_variance – operational

Cumulative explained-variance curve.

L3.5.B factor view cumulative_variance.

This option configures the factor_view 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

Quantifying how much variance the chosen n_components retains; threshold heuristics (80% / 90%) live here.

References

  • macroforecast design Part 4: ‘diagnostic layers default-off; non-blocking; produce JSON + matplotlib views attached to manifest.diagnostics/.’

  • Stock & Watson (2002) ‘Forecasting Using Principal Components from a Large Number of Predictors’, JASA 97(460): 1167-1179.

Related options: scree_plot, loadings_heatmap, factor_timeseries, multi

Last reviewed 2026-05-05 by macroforecast author.

factor_timeseries – operational

Estimated factor time-series plot.

L3.5.B factor view factor_timeseries.

This option configures the factor_view 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

Confirming factors track recognisable cycles (NBER recessions, oil-price spikes, etc.).

References

  • macroforecast design Part 4: ‘diagnostic layers default-off; non-blocking; produce JSON + matplotlib views attached to manifest.diagnostics/.’

  • Stock & Watson (2002) ‘Forecasting Using Principal Components from a Large Number of Predictors’, JASA 97(460): 1167-1179.

Related options: scree_plot, loadings_heatmap, cumulative_variance, multi

Last reviewed 2026-05-05 by macroforecast author.

loadings_heatmap – operational

Heatmap of factor loadings (factors × predictors).

L3.5.B factor view loadings_heatmap.

This option configures the factor_view 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

Interpreting factor identity; high-loading variables suggest the factor’s economic interpretation.

References

  • macroforecast design Part 4: ‘diagnostic layers default-off; non-blocking; produce JSON + matplotlib views attached to manifest.diagnostics/.’

  • Stock & Watson (2002) ‘Forecasting Using Principal Components from a Large Number of Predictors’, JASA 97(460): 1167-1179.

Related options: scree_plot, factor_timeseries, cumulative_variance, multi

Last reviewed 2026-05-05 by macroforecast author.

multi – operational

Render every factor view together.

L3.5.B factor view multi.

This option configures the factor_view 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

Default rich factor diagnostic; the standard package for factor-model papers.

References

  • macroforecast design Part 4: ‘diagnostic layers default-off; non-blocking; produce JSON + matplotlib views attached to manifest.diagnostics/.’

  • Stock & Watson (2002) ‘Forecasting Using Principal Components from a Large Number of Predictors’, JASA 97(460): 1167-1179.

Related options: scree_plot, loadings_heatmap, factor_timeseries, cumulative_variance

Last reviewed 2026-05-05 by macroforecast author.

scree_plot – operational

Eigenvalue scree plot for PCA / SPCA / DFM blocks.

L3.5.B factor view scree_plot.

This option configures the factor_view 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

Choosing n_components – the elbow heuristic remains the most popular tool.

References

  • macroforecast design Part 4: ‘diagnostic layers default-off; non-blocking; produce JSON + matplotlib views attached to manifest.diagnostics/.’

  • Stock & Watson (2002) ‘Forecasting Using Principal Components from a Large Number of Predictors’, JASA 97(460): 1167-1179.

Related options: loadings_heatmap, factor_timeseries, cumulative_variance, multi

Last reviewed 2026-05-05 by macroforecast author.